Apenas eco-regiões
%%HTML
<h1> Índice</h1>
<h3>
<a href="#1">clicar aqui!</a> para ir para as primeiras figuras
</h3><br>
<h3> outras figuras:</h3><br>
<a href="#varintermonbio">Variação inter-anual por mês em cada bioma</a>
<br>
<a href="#varintrabio"> Variação intra_anual em cada bioma</a>
# imports
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import numpy as np
import matplotlib.cm as cm
import matplotlib.colors as cls
from pandas import Series, DataFrame
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes, mark_inset
import matplotlib.ticker as ticker
from collections import defaultdict, namedtuple
from pandas import ExcelWriter
import xlsxwriter
import pandas as pd
import calendar
plt.rcParams["font.family"] = "Times New Roman"
def get_ecos():
m = Basemap(projection='cyl')
shpf = m.readshapefile('ecoregions2017/ecoregions2017','ecos',linewidth=0.1)
return m.ecos_info, m.ecos
months = "Jan Fev Mar Abr Mai Jun Jul Ago Set Out Nov Dez".split(' ')
ecos_info, ecos = get_ecos()
Eco = namedtuple('Eco', ['name', 'biome_name', 'biome_num', 'area'])
info_dict = {eco['ECO_ID']:Eco(eco['ECO_NAME'],eco['BIOME_NAME'],eco['BIOME_NUM'], eco['SHAPE_AREA']) for eco in ecos_info}
eco_names = []
for i in range(847):
name = info_dict[i].name
if type(name) == bytes:
name = name.decode('latin-1')
eco_names.append(name)
biome_names = []
for i in range(847):
biome_names.append(info_dict[i].biome_name)
biome_nums = []
for i in range(847):
biome_nums.append(info_dict[i].biome_num)
areas = []
for i in range(847):
areas.append(info_dict[i].area)
biome_nums[0] = 0.0
result = {}
for i in range(1,18):
suf = '20{0:02d}'.format(i)
in_file= 'result' + suf
result[suf] = DataFrame(np.load(in_file), columns =months)
for year in result:
result[year].index.name ='Eco_Id'
for year in result:
result[year] = result[year].transpose()
head = DataFrame()
head = head.assign(ECO_NAME = eco_names, BIOME_NUM=biome_nums,BIOME_NAME=biome_names,AREAS= areas).transpose()
result['Atributos'] = head
df_finall = pd.concat(result)
df_finall
| Eco_Id | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 837 | 838 | 839 | 840 | 841 | 842 | 843 | 844 | 845 | 846 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2001 | Jan | 36 | 1656 | 1332 | 0 | 0 | 0 | 378 | 1678 | 457 | 0 | ... | 0 | 0 | 0 | 0 | 107 | 0 | 0 | 0 | 0 | 0 |
| Fev | 0 | 965 | 1335 | 0 | 0 | 0 | 91 | 185 | 299 | 0 | ... | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 0 | 47 | 1294 | 24 | 0 | 0 | 0 | 30 | 232 | 0 | ... | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 0 | 519 | 363 | 114 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 0 | 3407 | 0 | 36 | 0 | 0 | 0 | 0 | 7 | 0 | ... | 4 | 0 | 0 | 0 | 84 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 80 | 28944 | 0 | 34 | 0 | 4 | 0 | 0 | 0 | 0 | ... | 2 | 0 | 0 | 0 | 187 | 0 | 0 | 0 | 0 | 0 | |
| Jul | 166 | 4019 | 0 | 343 | 8 | 0 | 0 | 0 | 0 | 6 | ... | 0 | 0 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | |
| Ago | 139 | 5190 | 0 | 616 | 0 | 6 | 0 | 0 | 4 | 42 | ... | 7 | 0 | 0 | 0 | 2 | 0 | 11 | 0 | 0 | 0 | |
| Set | 18 | 4374 | 0 | 181 | 5 | 0 | 0 | 0 | 1 | 103 | ... | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Out | 9 | 707 | 195 | 0 | 0 | 0 | 0 | 0 | 81 | 674 | ... | 0 | 0 | 0 | 0 | 55 | 3 | 0 | 0 | 0 | 0 | |
| Nov | 11 | 0 | 12343 | 0 | 0 | 0 | 10 | 6 | 11 | 1728 | ... | 0 | 0 | 0 | 0 | 185 | 0 | 0 | 0 | 0 | 0 | |
| Dez | 0 | 118 | 2649 | 0 | 0 | 15 | 53 | 396 | 1118 | 40 | ... | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 0 | 0 | 0 | |
| 2002 | Jan | 0 | 2640 | 1260 | 1 | 0 | 0 | 843 | 2051 | 1457 | 0 | ... | 0 | 0 | 0 | 0 | 288 | 0 | 0 | 0 | 0 | 0 |
| Fev | 22 | 2501 | 1641 | 12 | 0 | 0 | 0 | 63 | 1958 | 0 | ... | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 4 | 2 | 1014 | 25 | 0 | 0 | 0 | 6 | 36 | 0 | ... | 0 | 0 | 0 | 0 | 230 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 0 | 330 | 38 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 17 | 0 | 0 | 0 | 539 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 1 | 10298 | 0 | 676 | 0 | 11 | 0 | 0 | 3 | 0 | ... | 45 | 0 | 0 | 0 | 148 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 33 | 10677 | 0 | 405 | 0 | 32 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 1 | 366 | 0 | 1 | 0 | 0 | 0 | |
| Jul | 77 | 9630 | 0 | 758 | 0 | 42 | 0 | 0 | 15 | 0 | ... | 0 | 0 | 0 | 2 | 71 | 0 | 1 | 0 | 0 | 0 | |
| Ago | 56 | 8721 | 0 | 1498 | 0 | 23 | 0 | 0 | 137 | 6 | ... | 0 | 0 | 0 | 0 | 9 | 0 | 1 | 0 | 0 | 0 | |
| Set | 14 | 5632 | 0 | 216 | 0 | 0 | 0 | 0 | 607 | 83 | ... | 4 | 0 | 0 | 0 | 37 | 0 | 1 | 0 | 0 | 0 | |
| Out | 6 | 2374 | 44 | 66 | 0 | 0 | 0 | 0 | 210 | 200 | ... | 0 | 0 | 0 | 0 | 131 | 3 | 0 | 0 | 15 | 0 | |
| Nov | 0 | 1 | 4152 | 11 | 0 | 0 | 67 | 7 | 7 | 311 | ... | 0 | 0 | 0 | 0 | 297 | 0 | 0 | 0 | 0 | 0 | |
| Dez | 8 | 48 | 7445 | 97 | 0 | 0 | 391 | 739 | 75 | 41 | ... | 0 | 0 | 0 | 0 | 419 | 0 | 0 | 0 | 0 | 0 | |
| 2003 | Jan | 19 | 1791 | 1966 | 59 | 0 | 0 | 1716 | 1467 | 936 | 24 | ... | 0 | 0 | 0 | 0 | 122 | 0 | 252 | 0 | 0 | 0 |
| Fev | 0 | 4915 | 1822 | 369 | 0 | 13 | 61 | 322 | 1620 | 49 | ... | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 2 | 170 | 1897 | 405 | 0 | 24 | 37 | 165 | 527 | 14 | ... | 0 | 0 | 0 | 0 | 174 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 17 | 20 | 195 | 282 | 0 | 4 | 2 | 0 | 4 | 3 | ... | 0 | 0 | 0 | 0 | 172 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 4 | 9047 | 0 | 870 | 0 | 19 | 0 | 0 | 0 | 10 | ... | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 10 | 6591 | 0 | 225 | 13 | 208 | 0 | 0 | 0 | 6 | ... | 0 | 0 | 0 | 0 | 104 | 0 | 0 | 0 | 0 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2015 | Nov | 10 | 0 | 2147 | 1 | 0 | 2 | 225 | 69 | 0 | 116 | ... | 0 | 0 | 0 | 0 | 163 | 0 | 1 | 0 | 0 | 0 |
| Dez | 0 | 5 | 1153 | 25 | 0 | 2 | 117 | 452 | 5 | 0 | ... | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | |
| 2016 | Jan | 0 | 1824 | 2360 | 227 | 0 | 82 | 895 | 4987 | 483 | 16 | ... | 0 | 0 | 0 | 0 | 639 | 0 | 0 | 0 | 0 | 0 |
| Fev | 0 | 1747 | 1302 | 1141 | 0 | 76 | 20 | 298 | 2078 | 0 | ... | 0 | 0 | 0 | 0 | 434 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 0 | 116 | 38 | 450 | 0 | 0 | 0 | 0 | 554 | 0 | ... | 0 | 0 | 0 | 6 | 287 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 0 | 63 | 0 | 84 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 4 | 364 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 0 | 4820 | 0 | 1486 | 20 | 54 | 0 | 0 | 4 | 0 | ... | 0 | 0 | 0 | 0 | 216 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 14 | 8039 | 0 | 1924 | 10 | 500 | 0 | 0 | 6 | 0 | ... | 0 | 0 | 0 | 0 | 55 | 0 | 0 | 0 | 0 | 0 | |
| Jul | 43 | 6265 | 0 | 5993 | 0 | 1833 | 0 | 0 | 11 | 2 | ... | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | |
| Ago | 66 | 8081 | 0 | 1222 | 8 | 364 | 0 | 0 | 5 | 66 | ... | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | |
| Set | 5 | 5227 | 0 | 235 | 14 | 0 | 0 | 0 | 139 | 81 | ... | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | |
| Out | 2 | 3551 | 14 | 86 | 0 | 0 | 0 | 0 | 189 | 325 | ... | 0 | 0 | 0 | 0 | 262 | 0 | 0 | 0 | 0 | 0 | |
| Nov | 1 | 293 | 3039 | 28 | 0 | 0 | 5 | 0 | 309 | 536 | ... | 0 | 0 | 0 | 0 | 413 | 0 | 0 | 0 | 0 | 0 | |
| Dez | 0 | 487 | 1027 | 56 | 0 | 1 | 904 | 578 | 1015 | 222 | ... | 0 | 0 | 0 | 0 | 289 | 0 | 0 | 0 | 0 | 0 | |
| 2017 | Jan | 3 | 2525 | 618 | 221 | 0 | 10 | 106 | 1056 | 797 | 8 | ... | 0 | 0 | 0 | 0 | 648 | 0 | 0 | 0 | 0 | 0 |
| Fev | 17 | 269 | 1182 | 212 | 0 | 42 | 1006 | 2778 | 69 | 1 | ... | 0 | 0 | 0 | 0 | 210 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 1 | 2 | 842 | 621 | 0 | 69 | 19 | 138 | 45 | 0 | ... | 0 | 0 | 0 | 0 | 57 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 2 | 293 | 76 | 383 | 0 | 23 | 0 | 0 | 5 | 0 | ... | 0 | 0 | 0 | 0 | 221 | 1 | 18 | 0 | 0 | 0 | |
| Mai | 0 | 4934 | 0 | 886 | 0 | 55 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 719 | 1 | 0 | 0 | 0 | 0 | |
| Jun | 169 | 6861 | 0 | 2132 | 0 | 14 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 197 | 4 | 0 | 0 | 0 | 0 | |
| Jul | 103 | 5562 | 0 | 1804 | 1 | 606 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 15 | 0 | 5 | 0 | 0 | 0 | |
| Ago | 101 | 3482 | 0 | 1102 | 0 | 630 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | |
| Set | 251 | 4775 | 0 | 1670 | 0 | 0 | 0 | 0 | 2 | 53 | ... | 0 | 0 | 0 | 0 | 2 | 0 | 50 | 0 | 0 | 0 | |
| Out | 81 | 2597 | 34 | 23 | 0 | 0 | 0 | 0 | 0 | 284 | ... | 0 | 0 | 0 | 0 | 206 | 0 | 0 | 0 | 0 | 0 | |
| Nov | 1 | 32 | 2460 | 0 | 0 | 0 | 22 | 0 | 26 | 137 | ... | 0 | 0 | 0 | 0 | 323 | 3 | 0 | 0 | 0 | 0 | |
| Dez | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Atributos | AREAS | 6487.85 | 12.2498 | 3.09709 | 33.5636 | 0.17066 | 15.3531 | 1.68336 | 4.22837 | 5.01093 | 0.890325 | ... | 27.7603 | 25.9865 | 3.4904 | 31.7648 | 32.1171 | 253.974 | 78.3785 | 7.13004 | 68.5239 | 22.7455 |
| BIOME_NAME | N/A | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | ... | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | |
| BIOME_NUM | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
| ECO_NAME | Rock and Ice | Albertine Rift montane forests | Cameroon Highlands forests | Central Congolian lowland forests | Comoros forests | Congolian coastal forests | Cross-Niger transition forests | Cross-Sanaga-Bioko coastal forests | East African montane forests | Eastern Arc forests | ... | Red Sea-Arabian Desert shrublands | Registan-North Pakistan sandy desert | Saharan Atlantic coastal desert | South Arabian plains and plateau desert | South Iran Nubo-Sindian desert and semi-desert | South Sahara desert | Taklimakan desert | Tibesti-Jebel Uweinat montane xeric woodlands | West Sahara desert | West Saharan montane xeric woodlands |
208 rows × 847 columns
df_finall.loc['Atributos'].loc['BIOME_NUM']
Eco_Id
0 0
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
..
817 13
818 13
819 13
820 13
821 13
822 13
823 13
824 13
825 13
826 13
827 13
828 13
829 13
830 13
831 13
832 13
833 13
834 13
835 13
836 13
837 13
838 13
839 13
840 13
841 13
842 13
843 13
844 13
845 13
846 13
Name: BIOME_NUM, Length: 847, dtype: object
df_finall[:][:12*17]
biome_grouped = df_finall[:][:12*17].groupby(df_finall.loc['Atributos'].loc['BIOME_NUM'], axis=1)
biome_grouped.sum()
| BIOME_NUM | 0.0 | 1.0 | 2.0 | 3.0 | 4.0 | 5.0 | 6.0 | 7.0 | 8.0 | 9.0 | 10.0 | 11.0 | 12.0 | 13.0 | 14.0 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2001 | Jan | 36.0 | 44159.0 | 96226.0 | 148.0 | 1024.0 | 105.0 | 137.0 | 1467138.0 | 164076.0 | 139155.0 | 4084.0 | 0.0 | 28963.0 | 373792.0 | 191.0 |
| Fev | 0.0 | 97045.0 | 42788.0 | 508.0 | 1073.0 | 93.0 | 0.0 | 569728.0 | 24759.0 | 53868.0 | 3789.0 | 0.0 | 3882.0 | 16204.0 | 1735.0 | |
| Mar | 0.0 | 73085.0 | 18887.0 | 2066.0 | 7166.0 | 722.0 | 9.0 | 261311.0 | 18602.0 | 33808.0 | 1445.0 | 0.0 | 4467.0 | 22553.0 | 2379.0 | |
| Abr | 0.0 | 38649.0 | 17042.0 | 8286.0 | 31171.0 | 3079.0 | 6461.0 | 238841.0 | 50578.0 | 30531.0 | 331.0 | 0.0 | 6842.0 | 105658.0 | 1789.0 | |
| Mai | 0.0 | 16240.0 | 19069.0 | 11707.0 | 2195.0 | 970.0 | 2777.0 | 648311.0 | 13510.0 | 10107.0 | 3190.0 | 0.0 | 874.0 | 106221.0 | 719.0 | |
| Jun | 80.0 | 75637.0 | 14232.0 | 203.0 | 10333.0 | 1375.0 | 11724.0 | 1305506.0 | 48301.0 | 42774.0 | 23325.0 | 412.0 | 4912.0 | 93165.0 | 66.0 | |
| Jul | 166.0 | 44735.0 | 7605.0 | 1.0 | 36274.0 | 1862.0 | 81164.0 | 952967.0 | 193020.0 | 83115.0 | 12382.0 | 7020.0 | 6910.0 | 105022.0 | 65.0 | |
| Ago | 139.0 | 134837.0 | 30782.0 | 0.0 | 73278.0 | 7595.0 | 43006.0 | 2227245.0 | 224408.0 | 130052.0 | 25637.0 | 6517.0 | 11605.0 | 297078.0 | 293.0 | |
| Set | 18.0 | 99090.0 | 26636.0 | 32.0 | 49972.0 | 4491.0 | 5125.0 | 1870394.0 | 70850.0 | 63623.0 | 19781.0 | 56.0 | 10089.0 | 366418.0 | 149.0 | |
| Out | 9.0 | 55479.0 | 20708.0 | 15.0 | 39739.0 | 1330.0 | 976.0 | 1464865.0 | 15623.0 | 51243.0 | 3418.0 | 23.0 | 5426.0 | 212253.0 | 204.0 | |
| Nov | 11.0 | 53488.0 | 9964.0 | 44.0 | 8267.0 | 108.0 | 1.0 | 1438386.0 | 3049.0 | 13012.0 | 205.0 | 0.0 | 1672.0 | 256545.0 | 81.0 | |
| Dez | 0.0 | 44797.0 | 5740.0 | 14.0 | 18377.0 | 75.0 | 0.0 | 2063182.0 | 22281.0 | 52110.0 | 1691.0 | 0.0 | 1383.0 | 238849.0 | 84.0 | |
| 2002 | Jan | 0.0 | 63333.0 | 102876.0 | 148.0 | 14575.0 | 9.0 | 0.0 | 1387443.0 | 20526.0 | 181209.0 | 2822.0 | 0.0 | 7041.0 | 178296.0 | 835.0 |
| Fev | 22.0 | 69626.0 | 36954.0 | 230.0 | 5172.0 | 1133.0 | 3.0 | 642262.0 | 26235.0 | 127152.0 | 2602.0 | 0.0 | 5189.0 | 54090.0 | 2015.0 | |
| Mar | 4.0 | 71184.0 | 23280.0 | 2561.0 | 50260.0 | 6214.0 | 2828.0 | 275331.0 | 20494.0 | 62467.0 | 1327.0 | 0.0 | 5046.0 | 35658.0 | 2132.0 | |
| Abr | 0.0 | 53521.0 | 16540.0 | 5928.0 | 20649.0 | 4871.0 | 8610.0 | 260205.0 | 27855.0 | 45086.0 | 1205.0 | 0.0 | 6045.0 | 114910.0 | 1202.0 | |
| Mai | 1.0 | 35405.0 | 15729.0 | 15619.0 | 24964.0 | 8348.0 | 17879.0 | 783391.0 | 26776.0 | 35162.0 | 12696.0 | 339.0 | 1811.0 | 86389.0 | 1236.0 | |
| Jun | 33.0 | 30952.0 | 7829.0 | 3206.0 | 25702.0 | 18365.0 | 48479.0 | 860988.0 | 36880.0 | 50802.0 | 10531.0 | 1779.0 | 5698.0 | 38406.0 | 192.0 | |
| Jul | 77.0 | 66461.0 | 13117.0 | 794.0 | 35885.0 | 16107.0 | 144705.0 | 1462381.0 | 133996.0 | 93266.0 | 32546.0 | 2674.0 | 10880.0 | 181547.0 | 448.0 | |
| Ago | 56.0 | 190316.0 | 34746.0 | 68.0 | 78047.0 | 15391.0 | 117389.0 | 1844769.0 | 266976.0 | 102957.0 | 24626.0 | 16443.0 | 7825.0 | 474105.0 | 1426.0 | |
| Set | 14.0 | 219878.0 | 30303.0 | 2.0 | 67224.0 | 25349.0 | 28593.0 | 1520663.0 | 395617.0 | 58570.0 | 25622.0 | 1155.0 | 7020.0 | 316955.0 | 1604.0 | |
| Out | 6.0 | 150480.0 | 40547.0 | 11.0 | 26632.0 | 1557.0 | 2334.0 | 1331286.0 | 89852.0 | 55393.0 | 7816.0 | 0.0 | 10199.0 | 609036.0 | 2028.0 | |
| Nov | 0.0 | 68743.0 | 17956.0 | 47.0 | 17492.0 | 1462.0 | 295.0 | 1230167.0 | 21706.0 | 102711.0 | 3031.0 | 0.0 | 20901.0 | 564233.0 | 317.0 | |
| Dez | 8.0 | 80564.0 | 11942.0 | 27.0 | 8473.0 | 131.0 | 0.0 | 2396601.0 | 7875.0 | 221186.0 | 2073.0 | 0.0 | 23985.0 | 149991.0 | 439.0 | |
| 2003 | Jan | 19.0 | 114178.0 | 86283.0 | 117.0 | 40156.0 | 574.0 | 0.0 | 1793645.0 | 29681.0 | 77635.0 | 32753.0 | 0.0 | 12404.0 | 40966.0 | 1504.0 |
| Fev | 0.0 | 81908.0 | 74998.0 | 499.0 | 11679.0 | 565.0 | 21.0 | 677328.0 | 25727.0 | 42313.0 | 8569.0 | 0.0 | 15338.0 | 11804.0 | 1827.0 | |
| Mar | 2.0 | 230596.0 | 42861.0 | 9444.0 | 65325.0 | 11145.0 | 80199.0 | 313488.0 | 39357.0 | 61973.0 | 2123.0 | 635.0 | 9290.0 | 22216.0 | 6368.0 | |
| Abr | 17.0 | 111599.0 | 20957.0 | 8502.0 | 57143.0 | 3350.0 | 30556.0 | 213280.0 | 89623.0 | 56628.0 | 1715.0 | 48.0 | 3039.0 | 31189.0 | 4768.0 | |
| Mai | 4.0 | 87695.0 | 32801.0 | 22392.0 | 60749.0 | 40606.0 | 166185.0 | 880076.0 | 139087.0 | 32432.0 | 10712.0 | 3555.0 | 2278.0 | 35339.0 | 3502.0 | |
| Jun | 10.0 | 48593.0 | 8712.0 | 5133.0 | 39583.0 | 6040.0 | 99789.0 | 928390.0 | 29528.0 | 45973.0 | 10717.0 | 8919.0 | 7405.0 | 18216.0 | 582.0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2015 | Jul | 64.0 | 80281.0 | 9325.0 | 188.0 | 17009.0 | 13190.0 | 130222.0 | 1575435.0 | 66097.0 | 88169.0 | 12532.0 | 6723.0 | 9571.0 | 55333.0 | 545.0 |
| Ago | 23.0 | 123802.0 | 26581.0 | 43.0 | 24924.0 | 36110.0 | 35852.0 | 1804702.0 | 165693.0 | 68178.0 | 14723.0 | 9958.0 | 8278.0 | 111268.0 | 1213.0 | |
| Set | 68.0 | 192105.0 | 50785.0 | 1.0 | 62937.0 | 8708.0 | 9493.0 | 1675209.0 | 76360.0 | 42268.0 | 10854.0 | 854.0 | 12387.0 | 62713.0 | 3493.0 | |
| Out | 7.0 | 161492.0 | 40197.0 | 4.0 | 53782.0 | 1493.0 | 1490.0 | 1003642.0 | 50416.0 | 42075.0 | 9611.0 | 271.0 | 9333.0 | 247804.0 | 2354.0 | |
| Nov | 10.0 | 111930.0 | 27659.0 | 28.0 | 19997.0 | 353.0 | 0.0 | 1306803.0 | 10805.0 | 109322.0 | 4123.0 | 0.0 | 18802.0 | 226699.0 | 408.0 | |
| Dez | 0.0 | 103403.0 | 47243.0 | 81.0 | 2522.0 | 514.0 | 0.0 | 1448405.0 | 4655.0 | 202200.0 | 2781.0 | 0.0 | 2328.0 | 46917.0 | 896.0 | |
| 2016 | Jan | 0.0 | 136147.0 | 67805.0 | 228.0 | 6213.0 | 198.0 | 0.0 | 1783532.0 | 4527.0 | 110691.0 | 2738.0 | 0.0 | 10040.0 | 24001.0 | 1066.0 |
| Fev | 0.0 | 153625.0 | 48328.0 | 1076.0 | 7454.0 | 662.0 | 0.0 | 522278.0 | 11056.0 | 39469.0 | 1196.0 | 0.0 | 4023.0 | 11610.0 | 2833.0 | |
| Mar | 0.0 | 141674.0 | 28771.0 | 1958.0 | 25344.0 | 3820.0 | 4106.0 | 184718.0 | 35657.0 | 16137.0 | 768.0 | 1.0 | 3694.0 | 16440.0 | 4200.0 | |
| Abr | 0.0 | 152920.0 | 35248.0 | 16118.0 | 35583.0 | 4542.0 | 8354.0 | 233863.0 | 67298.0 | 25746.0 | 482.0 | 13.0 | 2998.0 | 40496.0 | 3997.0 | |
| Mai | 0.0 | 57105.0 | 22210.0 | 9471.0 | 29933.0 | 8938.0 | 38526.0 | 486084.0 | 43027.0 | 35287.0 | 6872.0 | 728.0 | 1797.0 | 42846.0 | 2381.0 | |
| Jun | 14.0 | 36794.0 | 9093.0 | 3645.0 | 5245.0 | 5981.0 | 33566.0 | 1052086.0 | 16300.0 | 80140.0 | 7679.0 | 4410.0 | 7948.0 | 8858.0 | 718.0 | |
| Jul | 43.0 | 82850.0 | 16921.0 | 78.0 | 12706.0 | 3058.0 | 98713.0 | 1409196.0 | 59234.0 | 64928.0 | 8924.0 | 15578.0 | 6451.0 | 23904.0 | 399.0 | |
| Ago | 66.0 | 128670.0 | 36267.0 | 220.0 | 27514.0 | 7157.0 | 13858.0 | 1726692.0 | 74818.0 | 49384.0 | 13312.0 | 16046.0 | 10555.0 | 39953.0 | 885.0 | |
| Set | 5.0 | 136870.0 | 28159.0 | 25.0 | 35833.0 | 3758.0 | 50355.0 | 1398895.0 | 40560.0 | 57196.0 | 8654.0 | 733.0 | 10142.0 | 24405.0 | 315.0 | |
| Out | 2.0 | 101033.0 | 38623.0 | 19.0 | 26115.0 | 1379.0 | 504.0 | 1153389.0 | 9249.0 | 62593.0 | 3612.0 | 14.0 | 6862.0 | 50592.0 | 454.0 | |
| Nov | 1.0 | 59699.0 | 21397.0 | 83.0 | 11171.0 | 428.0 | 449.0 | 1336845.0 | 6896.0 | 77622.0 | 1537.0 | 0.0 | 8505.0 | 115146.0 | 381.0 | |
| Dez | 0.0 | 53608.0 | 7002.0 | 183.0 | 4723.0 | 597.0 | 0.0 | 1815623.0 | 51369.0 | 144746.0 | 1893.0 | 0.0 | 13514.0 | 30927.0 | 634.0 | |
| 2017 | Jan | 3.0 | 46080.0 | 27916.0 | 1293.0 | 18841.0 | 428.0 | 0.0 | 906400.0 | 58233.0 | 104420.0 | 2665.0 | 0.0 | 9084.0 | 6077.0 | 804.0 |
| Fev | 17.0 | 125103.0 | 106284.0 | 1502.0 | 8437.0 | 1469.0 | 182.0 | 491538.0 | 21649.0 | 55574.0 | 1039.0 | 0.0 | 4910.0 | 12986.0 | 2058.0 | |
| Mar | 1.0 | 153770.0 | 45978.0 | 4789.0 | 66277.0 | 4759.0 | 7678.0 | 200176.0 | 70534.0 | 57956.0 | 1046.0 | 98.0 | 18462.0 | 18882.0 | 3625.0 | |
| Abr | 2.0 | 98054.0 | 51499.0 | 15632.0 | 81289.0 | 441.0 | 23614.0 | 175365.0 | 80497.0 | 40101.0 | 791.0 | 6.0 | 15405.0 | 27826.0 | 3005.0 | |
| Mai | 0.0 | 27408.0 | 17844.0 | 12186.0 | 8940.0 | 1293.0 | 5824.0 | 581731.0 | 28499.0 | 13330.0 | 5167.0 | 14.0 | 9749.0 | 78949.0 | 1093.0 | |
| Jun | 169.0 | 27737.0 | 7922.0 | 11539.0 | 17519.0 | 5887.0 | 26505.0 | 1027866.0 | 41388.0 | 36480.0 | 9710.0 | 2493.0 | 12575.0 | 90870.0 | 496.0 | |
| Jul | 103.0 | 72117.0 | 9004.0 | 49.0 | 9459.0 | 25513.0 | 65054.0 | 1290441.0 | 121570.0 | 76018.0 | 12582.0 | 10798.0 | 16018.0 | 254144.0 | 762.0 | |
| Ago | 101.0 | 105367.0 | 17681.0 | 38.0 | 16369.0 | 39391.0 | 102445.0 | 1588769.0 | 135978.0 | 70023.0 | 17006.0 | 1724.0 | 14763.0 | 187263.0 | 236.0 | |
| Set | 251.0 | 222013.0 | 42705.0 | 2.0 | 21651.0 | 19917.0 | 12861.0 | 1944863.0 | 120256.0 | 62172.0 | 12207.0 | 274.0 | 5943.0 | 333552.0 | 225.0 | |
| Out | 81.0 | 103263.0 | 28800.0 | 22.0 | 24724.0 | 1764.0 | 305.0 | 1298707.0 | 28434.0 | 38908.0 | 13578.0 | 0.0 | 21845.0 | 261457.0 | 209.0 | |
| Nov | 1.0 | 56768.0 | 13583.0 | 18.0 | 11593.0 | 1387.0 | 7.0 | 1151595.0 | 8171.0 | 29595.0 | 3970.0 | 0.0 | 11949.0 | 324904.0 | 221.0 | |
| Dez | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
204 rows × 15 columns
#writer = ExcelWriter('results_newstyle2.xlsx',engine='xlsxwriter')
#df_finall.to_excel(writer,sheet_name='Burned Pixels',engine='xlsxwriter')
#writer.save()
total_biome = biome_grouped.sum().sum()
total_biome
BIOME_NUM 0.0 4837.0 1.0 21872218.0 2.0 6702933.0 3.0 619787.0 4.0 6439650.0 5.0 1063191.0 6.0 4981719.0 7.0 237984112.0 8.0 11630411.0 9.0 13816076.0 10.0 1670577.0 11.0 437043.0 12.0 1623004.0 13.0 22379896.0 14.0 302628.0 dtype: float64
total_eco = df_finall.iloc[:12*17].sum()
total_eco
Eco_Id
0 4837.0
1 702891.0
2 225592.0
3 149934.0
4 1038.0
5 37142.0
6 22712.0
7 52385.0
8 60509.0
9 31478.0
10 18235.0
11 325106.0
12 7609.0
13 0.0
14 136999.0
15 1297.0
16 25166.0
17 138000.0
18 1967024.0
19 255883.0
20 548.0
21 0.0
22 8764.0
23 126461.0
24 1103731.0
25 561498.0
26 388095.0
27 3.0
28 1698135.0
29 123833.0
...
817 363213.0
818 91760.0
819 37002.0
820 6250.0
821 0.0
822 1424.0
823 6901.0
824 494.0
825 159.0
826 13431.0
827 39677.0
828 2355221.0
829 11675.0
830 13690.0
831 36318.0
832 0.0
833 511.0
834 4485.0
835 215.0
836 10.0
837 314.0
838 4247.0
839 0.0
840 29.0
841 42379.0
842 324.0
843 3099.0
844 0.0
845 25.0
846 0.0
Length: 847, dtype: float64
total_eco_area = total_eco/(areas)
df_finall[:][12*17:-1:2]
| Eco_Id | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 837 | 838 | 839 | 840 | 841 | 842 | 843 | 844 | 845 | 846 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Atributos | AREAS | 6487.85 | 12.2498 | 3.09709 | 33.5636 | 0.17066 | 15.3531 | 1.68336 | 4.22837 | 5.01093 | 0.890325 | ... | 27.7603 | 25.9865 | 3.4904 | 31.7648 | 32.1171 | 253.974 | 78.3785 | 7.13004 | 68.5239 | 22.7455 |
| BIOME_NUM | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 |
2 rows × 847 columns
df_finall[:][12*17 +2: 12*17 +3]
| Eco_Id | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 837 | 838 | 839 | 840 | 841 | 842 | 843 | 844 | 845 | 846 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Atributos | BIOME_NUM | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 |
1 rows × 847 columns
df_finall.loc['Atributos'].loc['AREAS']
Eco_Id
0 6487.85
1 12.2498
2 3.09709
3 33.5636
4 0.17066
5 15.3531
6 1.68336
7 4.22837
8 5.01093
9 0.890325
10 7.50154
11 15.4412
12 5.54681
13 0.0251181
14 2.55149
15 0.201715
16 1.0287
17 9.59984
18 17.1053
19 2.72629
20 0.427807
21 0.0929414
22 1.1698
23 5.48488
24 41.6676
25 11.6915
26 35.1508
27 0.0838619
28 12.6843
29 10.4078
...
817 75.4713
818 9.8943
819 60.3621
820 56.3572
821 1.53083
822 137.457
823 2.35131
824 31.6342
825 16.0622
826 19.2962
827 35.2106
828 82.3318
829 2.70801
830 19.9401
831 43.9754
832 0.735171
833 148.952
834 9.25653
835 19.6334
836 5.2527
837 27.7603
838 25.9865
839 3.4904
840 31.7648
841 32.1171
842 253.974
843 78.3785
844 7.13004
845 68.5239
846 22.7455
Name: AREAS, Length: 847, dtype: object
df_finall.loc['Atributos'].loc['AREAS'].groupby(biome_nums).sum()
0.0 6487.848621 1.0 1632.472566 2.0 327.121754 3.0 60.219125 4.0 1444.893009 5.0 444.991141 6.0 2522.867989 7.0 1796.152160 8.0 1198.433201 9.0 105.597352 10.0 471.456944 11.0 1993.369285 12.0 329.432038 13.0 2488.539746 14.0 27.665056 Name: AREAS, dtype: float64
df_finall.T[df_finall.T['Atributos']['BIOME_NUM'] == 14]['Atributos']['AREAS'].sum()
27.665055532789804
areas_biome = df_finall.loc['Atributos'].loc['AREAS'].groupby(biome_nums).sum()
areas_biome
0.0 6487.848621 1.0 1632.472566 2.0 327.121754 3.0 60.219125 4.0 1444.893009 5.0 444.991141 6.0 2522.867989 7.0 1796.152160 8.0 1198.433201 9.0 105.597352 10.0 471.456944 11.0 1993.369285 12.0 329.432038 13.0 2488.539746 14.0 27.665056 Name: AREAS, dtype: float64
total_biome_area = total_biome/areas_biome
total_biome.iloc[14]/areas_biome[14]
10938.998464735138
302628.0/27.665056
10938.998279996253
df_finall[1: 12*17:12]
Eco_Id
0 114.0
1 39753.0
2 28266.0
3 7100.0
4 0.0
5 382.0
6 4368.0
7 15774.0
8 17994.0
9 82.0
10 1213.0
11 94081.0
12 3736.0
13 0.0
14 15642.0
15 78.0
16 21.0
17 59.0
18 75.0
19 3945.0
20 0.0
21 0.0
22 1800.0
23 28586.0
24 202063.0
25 32492.0
26 50314.0
27 1.0
28 276.0
29 15963.0
...
817 2204.0
818 7023.0
819 11749.0
820 582.0
821 0.0
822 0.0
823 1074.0
824 0.0
825 0.0
826 0.0
827 232.0
828 353.0
829 1204.0
830 1484.0
831 861.0
832 0.0
833 64.0
834 20.0
835 202.0
836 0.0
837 0.0
838 777.0
839 0.0
840 0.0
841 6551.0
842 0.0
843 1899.0
844 0.0
845 2.0
846 0.0
Length: 847, dtype: float64
months
['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez']
monthly = [ df_finall[n: 12*17:12].sum()/areas for n in range(12)]
monthly
[Eco_Id
0 0.023428
1 2317.345349
2 8149.267504
3 59.320297
4 0.000000
5 15.762279
6 7294.335597
7 6441.491295
8 3084.058386
9 139.274984
10 33.726413
11 7769.135310
12 66.164109
13 0.000000
14 11038.273505
15 287.534440
16 51.521157
17 293.546527
18 52.556688
19 1268.754934
20 0.000000
21 0.000000
22 2939.816309
23 11913.288339
24 6395.825687
25 1857.676390
26 1981.720437
27 0.000000
28 64.252635
29 479.542527
...
817 5.180774
818 78.327908
819 39.677186
820 17.832687
821 0.000000
822 0.000000
823 409.132801
824 0.000000
825 0.000000
826 0.000000
827 0.000000
828 0.000000
829 34.711889
830 34.453112
831 10.005595
832 0.000000
833 0.120844
834 4.321276
835 0.000000
836 0.000000
837 0.000000
838 10.967231
839 0.000000
840 0.000000
841 173.895088
842 0.003937
843 3.215166
844 0.000000
845 0.000000
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.017571
1 3245.197789
2 9126.637160
3 211.538979
4 0.000000
5 24.880953
6 2594.808852
7 3730.516712
8 3590.950343
9 92.101199
10 161.700156
11 6092.843905
12 673.539815
13 0.000000
14 6130.545170
15 386.684246
16 20.414043
17 6.145935
18 4.384596
19 1447.018854
20 0.000000
21 0.000000
22 1538.723279
23 5211.778774
24 4849.398031
25 2779.116039
26 1431.376880
27 11.924364
28 21.759175
29 1533.748219
...
817 29.203136
818 709.802452
819 194.641864
820 10.326989
821 0.000000
822 0.000000
823 456.765726
824 0.000000
825 0.000000
826 0.000000
827 6.588920
828 4.287530
829 444.607594
830 74.422733
831 19.579130
832 0.000000
833 0.429668
834 2.160638
835 10.288591
836 0.000000
837 0.000000
838 29.900135
839 0.000000
840 0.000000
841 203.972555
842 0.000000
843 24.228571
844 0.000000
845 0.029187
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.024970
1 152.655645
2 6322.713113
3 209.602355
4 0.000000
5 15.892546
6 438.408638
7 464.718229
8 1266.631201
9 55.036082
10 190.894166
11 1429.419869
12 574.924644
13 0.000000
14 3252.222466
15 401.556717
16 81.656173
17 4.375072
18 54.193604
19 4064.490727
20 0.000000
21 0.000000
22 20.516310
23 313.042194
24 1462.453630
25 1481.163131
26 615.662183
27 23.848729
28 75.920598
29 2440.757126
...
817 288.361090
818 2981.817135
819 52.549493
820 3.761721
821 0.000000
822 0.000000
823 297.280486
824 0.505782
825 0.373548
826 14.458807
827 48.678487
828 95.406644
829 33.973338
830 6.118311
831 11.165334
832 0.000000
833 0.181266
834 0.648191
835 0.000000
836 0.000000
837 0.000000
838 3.424855
839 0.000000
840 0.188888
841 83.382461
842 0.000000
843 3.980682
844 0.000000
845 0.116748
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.006320
1 732.583829
2 1085.212888
3 105.560930
4 11.719240
5 14.003678
6 6.534546
7 0.000000
8 37.917115
9 3.369556
10 87.981948
11 441.674679
12 46.513189
13 0.000000
14 796.398656
15 510.621505
16 314.959526
17 3.229220
18 859.030001
19 3040.390184
20 0.000000
21 0.000000
22 0.000000
23 51.778674
24 86.589965
25 198.007313
26 166.767327
27 0.000000
28 409.088250
29 814.674632
...
817 327.091020
818 2543.787258
819 12.077106
820 0.337135
821 0.000000
822 0.000000
823 387.442808
824 7.839626
825 0.747096
826 98.724112
827 134.249245
828 91.787143
829 0.000000
830 78.635341
831 8.868596
832 0.000000
833 0.040281
834 19.121645
835 0.000000
836 0.000000
837 0.612386
838 1.731668
839 0.000000
840 0.157407
841 99.043917
842 0.094498
843 0.663447
844 0.000000
845 0.000000
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.006628
1 7150.814921
2 2.905955
3 350.975940
4 169.928987
5 70.148655
6 0.000000
7 0.000000
8 9.179933
9 12.355039
10 11.864232
11 11.980911
12 1.261986
13 0.000000
14 63.100484
15 391.641737
16 1941.278314
17 10.833513
18 4605.111697
19 6522.772186
20 0.000000
21 0.000000
22 0.000000
23 0.000000
24 35.903156
25 491.639757
26 115.502447
27 0.000000
28 1937.039563
29 219.257874
...
817 116.203040
818 203.652562
819 18.869443
820 3.939161
821 0.000000
822 6.045518
823 344.062823
824 0.063223
825 2.552578
826 61.722004
827 73.926546
828 373.780274
829 299.482358
830 141.874604
831 145.786067
832 0.000000
833 0.342392
834 14.692338
835 0.000000
836 0.190378
837 7.924995
838 0.230889
839 0.000000
840 0.000000
841 131.954768
842 0.031499
843 0.000000
844 0.000000
845 0.000000
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.082770
1 11923.548771
2 0.000000
3 756.564690
4 175.788607
5 537.220153
6 0.000000
7 0.000000
8 9.778624
9 26.956448
10 187.028292
11 0.000000
12 0.360567
13 0.000000
14 0.000000
15 2632.427369
16 6725.941240
17 5.416756
18 7212.075414
19 11280.511489
20 0.000000
21 0.000000
22 0.000000
23 0.000000
24 27.167362
25 5340.466799
26 420.332182
27 0.000000
28 5320.591210
29 511.729814
...
817 1046.516362
818 125.829995
819 110.201522
820 17.158416
821 0.000000
822 1.025774
823 9.356467
824 0.379337
825 5.727737
826 105.564837
827 136.720090
828 6976.417993
829 566.099204
830 134.803441
831 221.442009
832 0.000000
833 0.000000
834 22.146538
835 0.000000
836 0.571135
837 0.972613
838 0.692667
839 0.000000
840 0.409258
841 127.440035
842 0.031499
843 0.051034
844 0.000000
845 0.000000
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.200220
1 10721.732509
2 0.968652
3 1837.171343
4 410.173416
5 1365.912201
6 0.000000
7 0.000000
8 23.748088
9 356.049757
10 803.168540
11 0.000000
12 1.261986
13 0.000000
14 0.000000
15 29.744942
16 6504.303055
17 18.125300
18 12130.890322
19 14094.587191
20 11.687516
21 0.000000
22 0.000000
23 0.000000
24 107.205480
25 12639.452589
26 1621.699878
27 0.000000
28 11275.273149
29 3349.207256
...
817 976.768038
818 174.443832
819 76.620872
820 21.736360
821 0.000000
822 0.240075
823 0.000000
824 0.316114
825 0.000000
826 197.500047
827 97.640978
828 8134.293910
829 1429.834390
830 82.647348
831 189.105745
832 0.000000
833 0.792200
834 100.577694
835 0.000000
836 0.761513
837 0.576363
838 40.059254
839 0.000000
840 0.062963
841 22.355716
842 0.421303
843 3.138614
844 0.000000
845 0.000000
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.214555
1 9745.226184
2 0.000000
3 607.355224
4 884.802654
5 354.260477
6 0.000000
7 0.000000
8 97.187552
9 1590.430461
10 746.380192
11 0.000000
12 2.163404
13 0.000000
14 0.000000
15 252.832007
16 6145.599150
17 243.233194
18 19894.489421
19 21218.908406
20 151.937711
21 0.000000
22 0.000000
23 0.000000
24 64.126493
25 9477.750497
26 2206.551925
27 0.000000
28 23882.586204
29 2258.106286
...
817 761.944790
818 274.097145
819 46.486090
820 4.897335
821 0.000000
822 0.000000
823 0.000000
824 1.928295
825 0.000000
826 91.106030
827 302.323506
828 8604.804739
829 755.168215
830 49.799039
831 121.886339
832 0.000000
833 0.147698
834 42.132439
835 0.000000
836 0.000000
837 0.576363
838 41.406106
839 0.000000
840 0.062963
841 11.208994
842 0.007875
843 0.472068
844 0.000000
845 0.000000
846 0.000000
Length: 847, dtype: float64, Eco_Id
0 0.117142
1 6354.230008
2 0.000000
3 283.730380
4 1441.466576
5 12.896410
6 0.000000
7 0.000000
8 346.442692
9 6681.829668
10 178.496710
11 0.000000
12 0.360567
13 0.000000
14 0.000000
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Length: 847, dtype: float64, Eco_Id
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Length: 847, dtype: float64]
dict_monthly = dict(zip(months,monthly))
dict_monthly['Jan']
Eco_Id
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1 2317.345349
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3 59.320297
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840 0.000000
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842 0.003937
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846 0.000000
Length: 847, dtype: float64
monthly_biome = [ df_finall[n: 12*17:12].sum() for n in range(12)]
monthly_biome = [mb.groupby(biome_nums).sum()/areas_biome for mb in monthly_biome]
monthly_biome
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dict_monthly_biome = dict(zip(months,monthly_biome))
annual = [ df_finall[n*12: (n+1) *12].sum()/areas for n in range(17)]
annual
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Length: 847, dtype: float64, Eco_Id
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Length: 847, dtype: float64, Eco_Id
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Length: 847, dtype: float64, Eco_Id
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Length: 847, dtype: float64, Eco_Id
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Length: 847, dtype: float64, Eco_Id
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...
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...
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842 0.062999
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Length: 847, dtype: float64, Eco_Id
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...
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years = [str(y) for y in range(2001,2018)]
years
['2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017']
dict_annual = dict(zip(years,annual))
dict_annual['2002']
Eco_Id
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3 113.962901
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Length: 847, dtype: float64
annual_biome = [ df_finall[n*12: (n+1) *12].sum() for n in range(17)]
annual_biome = [ ab.groupby(biome_nums).sum()/areas_biome for ab in annual_biome]
dict_annual_biome = dict(zip(years,annual_biome))
annual_total = [ df_finall[n*12: (n+1) *12].sum().sum() for n in range(17)]
fig, ax = plt.subplots()
ax.plot(years, annual_total)
labels = ax.get_xticklabels()
plt.tight_layout()
plt.setp(labels, rotation=30, fontsize=10);plt.show()
monthly_total = [ df_finall[n: 12*17:12].sum().sum() for n in range(12)]
fig, ax = plt.subplots()
#ax[0].plot( np.arange(1,13),monthly_total)
#labels = ax.get_xticklabels()
#plt.setp(labels, rotation=30, fontsize=10)
#plt.xticks(months,months)
ax.plot( np.arange(12),monthly_total)
ax.set_xticks(np.arange(12))
ax.set_xticklabels(months)
plt.show()
months
['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez']
all_months = [df_finall.iloc[m].sum() for m in range(17*12)]
import itertools
ym = itertools.product(years,months)
ymlabels = [y + m for y, m in ym]
ymlabels
from statistics import mean
media = mean(all_months)
fig, ax = plt.subplots()
ax.plot( np.arange(12*17),all_months)
ax.axhline(y=media, color='r', linestyle='--')
ax.set_xticks(np.arange(12*17))
ax.set_xticklabels(ymlabels)
plt.setp(labels, rotation=60);
plt.show()
['2001Jan', '2001Fev', '2001Mar', '2001Abr', '2001Mai', '2001Jun', '2001Jul', '2001Ago', '2001Set', '2001Out', '2001Nov', '2001Dez', '2002Jan', '2002Fev', '2002Mar', '2002Abr', '2002Mai', '2002Jun', '2002Jul', '2002Ago', '2002Set', '2002Out', '2002Nov', '2002Dez', '2003Jan', '2003Fev', '2003Mar', '2003Abr', '2003Mai', '2003Jun', '2003Jul', '2003Ago', '2003Set', '2003Out', '2003Nov', '2003Dez', '2004Jan', '2004Fev', '2004Mar', '2004Abr', '2004Mai', '2004Jun', '2004Jul', '2004Ago', '2004Set', '2004Out', '2004Nov', '2004Dez', '2005Jan', '2005Fev', '2005Mar', '2005Abr', '2005Mai', '2005Jun', '2005Jul', '2005Ago', '2005Set', '2005Out', '2005Nov', '2005Dez', '2006Jan', '2006Fev', '2006Mar', '2006Abr', '2006Mai', '2006Jun', '2006Jul', '2006Ago', '2006Set', '2006Out', '2006Nov', '2006Dez', '2007Jan', '2007Fev', '2007Mar', '2007Abr', '2007Mai', '2007Jun', '2007Jul', '2007Ago', '2007Set', '2007Out', '2007Nov', '2007Dez', '2008Jan', '2008Fev', '2008Mar', '2008Abr', '2008Mai', '2008Jun', '2008Jul', '2008Ago', '2008Set', '2008Out', '2008Nov', '2008Dez', '2009Jan', '2009Fev', '2009Mar', '2009Abr', '2009Mai', '2009Jun', '2009Jul', '2009Ago', '2009Set', '2009Out', '2009Nov', '2009Dez', '2010Jan', '2010Fev', '2010Mar', '2010Abr', '2010Mai', '2010Jun', '2010Jul', '2010Ago', '2010Set', '2010Out', '2010Nov', '2010Dez', '2011Jan', '2011Fev', '2011Mar', '2011Abr', '2011Mai', '2011Jun', '2011Jul', '2011Ago', '2011Set', '2011Out', '2011Nov', '2011Dez', '2012Jan', '2012Fev', '2012Mar', '2012Abr', '2012Mai', '2012Jun', '2012Jul', '2012Ago', '2012Set', '2012Out', '2012Nov', '2012Dez', '2013Jan', '2013Fev', '2013Mar', '2013Abr', '2013Mai', '2013Jun', '2013Jul', '2013Ago', '2013Set', '2013Out', '2013Nov', '2013Dez', '2014Jan', '2014Fev', '2014Mar', '2014Abr', '2014Mai', '2014Jun', '2014Jul', '2014Ago', '2014Set', '2014Out', '2014Nov', '2014Dez', '2015Jan', '2015Fev', '2015Mar', '2015Abr', '2015Mai', '2015Jun', '2015Jul', '2015Ago', '2015Set', '2015Out', '2015Nov', '2015Dez', '2016Jan', '2016Fev', '2016Mar', '2016Abr', '2016Mai', '2016Jun', '2016Jul', '2016Ago', '2016Set', '2016Out', '2016Nov', '2016Dez', '2017Jan', '2017Fev', '2017Mar', '2017Abr', '2017Mai', '2017Jun', '2017Jul', '2017Ago', '2017Set', '2017Out', '2017Nov', '2017Dez']
df_finall
| Eco_Id | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 837 | 838 | 839 | 840 | 841 | 842 | 843 | 844 | 845 | 846 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2001 | Jan | 36 | 1656 | 1332 | 0 | 0 | 0 | 378 | 1678 | 457 | 0 | ... | 0 | 0 | 0 | 0 | 107 | 0 | 0 | 0 | 0 | 0 |
| Fev | 0 | 965 | 1335 | 0 | 0 | 0 | 91 | 185 | 299 | 0 | ... | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 0 | 47 | 1294 | 24 | 0 | 0 | 0 | 30 | 232 | 0 | ... | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 0 | 519 | 363 | 114 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 0 | 3407 | 0 | 36 | 0 | 0 | 0 | 0 | 7 | 0 | ... | 4 | 0 | 0 | 0 | 84 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 80 | 28944 | 0 | 34 | 0 | 4 | 0 | 0 | 0 | 0 | ... | 2 | 0 | 0 | 0 | 187 | 0 | 0 | 0 | 0 | 0 | |
| Jul | 166 | 4019 | 0 | 343 | 8 | 0 | 0 | 0 | 0 | 6 | ... | 0 | 0 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | |
| Ago | 139 | 5190 | 0 | 616 | 0 | 6 | 0 | 0 | 4 | 42 | ... | 7 | 0 | 0 | 0 | 2 | 0 | 11 | 0 | 0 | 0 | |
| Set | 18 | 4374 | 0 | 181 | 5 | 0 | 0 | 0 | 1 | 103 | ... | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Out | 9 | 707 | 195 | 0 | 0 | 0 | 0 | 0 | 81 | 674 | ... | 0 | 0 | 0 | 0 | 55 | 3 | 0 | 0 | 0 | 0 | |
| Nov | 11 | 0 | 12343 | 0 | 0 | 0 | 10 | 6 | 11 | 1728 | ... | 0 | 0 | 0 | 0 | 185 | 0 | 0 | 0 | 0 | 0 | |
| Dez | 0 | 118 | 2649 | 0 | 0 | 15 | 53 | 396 | 1118 | 40 | ... | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 0 | 0 | 0 | |
| 2002 | Jan | 0 | 2640 | 1260 | 1 | 0 | 0 | 843 | 2051 | 1457 | 0 | ... | 0 | 0 | 0 | 0 | 288 | 0 | 0 | 0 | 0 | 0 |
| Fev | 22 | 2501 | 1641 | 12 | 0 | 0 | 0 | 63 | 1958 | 0 | ... | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 4 | 2 | 1014 | 25 | 0 | 0 | 0 | 6 | 36 | 0 | ... | 0 | 0 | 0 | 0 | 230 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 0 | 330 | 38 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 17 | 0 | 0 | 0 | 539 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 1 | 10298 | 0 | 676 | 0 | 11 | 0 | 0 | 3 | 0 | ... | 45 | 0 | 0 | 0 | 148 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 33 | 10677 | 0 | 405 | 0 | 32 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 1 | 366 | 0 | 1 | 0 | 0 | 0 | |
| Jul | 77 | 9630 | 0 | 758 | 0 | 42 | 0 | 0 | 15 | 0 | ... | 0 | 0 | 0 | 2 | 71 | 0 | 1 | 0 | 0 | 0 | |
| Ago | 56 | 8721 | 0 | 1498 | 0 | 23 | 0 | 0 | 137 | 6 | ... | 0 | 0 | 0 | 0 | 9 | 0 | 1 | 0 | 0 | 0 | |
| Set | 14 | 5632 | 0 | 216 | 0 | 0 | 0 | 0 | 607 | 83 | ... | 4 | 0 | 0 | 0 | 37 | 0 | 1 | 0 | 0 | 0 | |
| Out | 6 | 2374 | 44 | 66 | 0 | 0 | 0 | 0 | 210 | 200 | ... | 0 | 0 | 0 | 0 | 131 | 3 | 0 | 0 | 15 | 0 | |
| Nov | 0 | 1 | 4152 | 11 | 0 | 0 | 67 | 7 | 7 | 311 | ... | 0 | 0 | 0 | 0 | 297 | 0 | 0 | 0 | 0 | 0 | |
| Dez | 8 | 48 | 7445 | 97 | 0 | 0 | 391 | 739 | 75 | 41 | ... | 0 | 0 | 0 | 0 | 419 | 0 | 0 | 0 | 0 | 0 | |
| 2003 | Jan | 19 | 1791 | 1966 | 59 | 0 | 0 | 1716 | 1467 | 936 | 24 | ... | 0 | 0 | 0 | 0 | 122 | 0 | 252 | 0 | 0 | 0 |
| Fev | 0 | 4915 | 1822 | 369 | 0 | 13 | 61 | 322 | 1620 | 49 | ... | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 2 | 170 | 1897 | 405 | 0 | 24 | 37 | 165 | 527 | 14 | ... | 0 | 0 | 0 | 0 | 174 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 17 | 20 | 195 | 282 | 0 | 4 | 2 | 0 | 4 | 3 | ... | 0 | 0 | 0 | 0 | 172 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 4 | 9047 | 0 | 870 | 0 | 19 | 0 | 0 | 0 | 10 | ... | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 10 | 6591 | 0 | 225 | 13 | 208 | 0 | 0 | 0 | 6 | ... | 0 | 0 | 0 | 0 | 104 | 0 | 0 | 0 | 0 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2015 | Nov | 10 | 0 | 2147 | 1 | 0 | 2 | 225 | 69 | 0 | 116 | ... | 0 | 0 | 0 | 0 | 163 | 0 | 1 | 0 | 0 | 0 |
| Dez | 0 | 5 | 1153 | 25 | 0 | 2 | 117 | 452 | 5 | 0 | ... | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | |
| 2016 | Jan | 0 | 1824 | 2360 | 227 | 0 | 82 | 895 | 4987 | 483 | 16 | ... | 0 | 0 | 0 | 0 | 639 | 0 | 0 | 0 | 0 | 0 |
| Fev | 0 | 1747 | 1302 | 1141 | 0 | 76 | 20 | 298 | 2078 | 0 | ... | 0 | 0 | 0 | 0 | 434 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 0 | 116 | 38 | 450 | 0 | 0 | 0 | 0 | 554 | 0 | ... | 0 | 0 | 0 | 6 | 287 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 0 | 63 | 0 | 84 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 4 | 364 | 0 | 0 | 0 | 0 | 0 | |
| Mai | 0 | 4820 | 0 | 1486 | 20 | 54 | 0 | 0 | 4 | 0 | ... | 0 | 0 | 0 | 0 | 216 | 0 | 0 | 0 | 0 | 0 | |
| Jun | 14 | 8039 | 0 | 1924 | 10 | 500 | 0 | 0 | 6 | 0 | ... | 0 | 0 | 0 | 0 | 55 | 0 | 0 | 0 | 0 | 0 | |
| Jul | 43 | 6265 | 0 | 5993 | 0 | 1833 | 0 | 0 | 11 | 2 | ... | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | |
| Ago | 66 | 8081 | 0 | 1222 | 8 | 364 | 0 | 0 | 5 | 66 | ... | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | |
| Set | 5 | 5227 | 0 | 235 | 14 | 0 | 0 | 0 | 139 | 81 | ... | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | |
| Out | 2 | 3551 | 14 | 86 | 0 | 0 | 0 | 0 | 189 | 325 | ... | 0 | 0 | 0 | 0 | 262 | 0 | 0 | 0 | 0 | 0 | |
| Nov | 1 | 293 | 3039 | 28 | 0 | 0 | 5 | 0 | 309 | 536 | ... | 0 | 0 | 0 | 0 | 413 | 0 | 0 | 0 | 0 | 0 | |
| Dez | 0 | 487 | 1027 | 56 | 0 | 1 | 904 | 578 | 1015 | 222 | ... | 0 | 0 | 0 | 0 | 289 | 0 | 0 | 0 | 0 | 0 | |
| 2017 | Jan | 3 | 2525 | 618 | 221 | 0 | 10 | 106 | 1056 | 797 | 8 | ... | 0 | 0 | 0 | 0 | 648 | 0 | 0 | 0 | 0 | 0 |
| Fev | 17 | 269 | 1182 | 212 | 0 | 42 | 1006 | 2778 | 69 | 1 | ... | 0 | 0 | 0 | 0 | 210 | 0 | 0 | 0 | 0 | 0 | |
| Mar | 1 | 2 | 842 | 621 | 0 | 69 | 19 | 138 | 45 | 0 | ... | 0 | 0 | 0 | 0 | 57 | 0 | 0 | 0 | 0 | 0 | |
| Abr | 2 | 293 | 76 | 383 | 0 | 23 | 0 | 0 | 5 | 0 | ... | 0 | 0 | 0 | 0 | 221 | 1 | 18 | 0 | 0 | 0 | |
| Mai | 0 | 4934 | 0 | 886 | 0 | 55 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 719 | 1 | 0 | 0 | 0 | 0 | |
| Jun | 169 | 6861 | 0 | 2132 | 0 | 14 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 197 | 4 | 0 | 0 | 0 | 0 | |
| Jul | 103 | 5562 | 0 | 1804 | 1 | 606 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 15 | 0 | 5 | 0 | 0 | 0 | |
| Ago | 101 | 3482 | 0 | 1102 | 0 | 630 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | |
| Set | 251 | 4775 | 0 | 1670 | 0 | 0 | 0 | 0 | 2 | 53 | ... | 0 | 0 | 0 | 0 | 2 | 0 | 50 | 0 | 0 | 0 | |
| Out | 81 | 2597 | 34 | 23 | 0 | 0 | 0 | 0 | 0 | 284 | ... | 0 | 0 | 0 | 0 | 206 | 0 | 0 | 0 | 0 | 0 | |
| Nov | 1 | 32 | 2460 | 0 | 0 | 0 | 22 | 0 | 26 | 137 | ... | 0 | 0 | 0 | 0 | 323 | 3 | 0 | 0 | 0 | 0 | |
| Dez | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Atributos | AREAS | 6487.85 | 12.2498 | 3.09709 | 33.5636 | 0.17066 | 15.3531 | 1.68336 | 4.22837 | 5.01093 | 0.890325 | ... | 27.7603 | 25.9865 | 3.4904 | 31.7648 | 32.1171 | 253.974 | 78.3785 | 7.13004 | 68.5239 | 22.7455 |
| BIOME_NAME | N/A | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | Tropical & Subtropical Moist Broadleaf Forests | ... | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | Deserts & Xeric Shrublands | |
| BIOME_NUM | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
| ECO_NAME | Rock and Ice | Albertine Rift montane forests | Cameroon Highlands forests | Central Congolian lowland forests | Comoros forests | Congolian coastal forests | Cross-Niger transition forests | Cross-Sanaga-Bioko coastal forests | East African montane forests | Eastern Arc forests | ... | Red Sea-Arabian Desert shrublands | Registan-North Pakistan sandy desert | Saharan Atlantic coastal desert | South Arabian plains and plateau desert | South Iran Nubo-Sindian desert and semi-desert | South Sahara desert | Taklimakan desert | Tibesti-Jebel Uweinat montane xeric woodlands | West Sahara desert | West Saharan montane xeric woodlands |
208 rows × 847 columns
annual_total
[20024630.0, 21610924.0, 20411080.0, 21490071.0, 21322025.0, 20108210.0, 21651364.0, 19752869.0, 17812399.0, 19432613.0, 21420223.0, 21207740.0, 16579315.0, 17711851.0, 18036370.0, 17067128.0, 15889270.0]
n = 0
df_finall[n*12: (n+1) *12].sum().sum()
20024630.0
ar_annual_biome = np.array(annual_biome)
#testing
lixo = np.array(annual_biome)
lixo.shape
(17, 15)
ar_annual_biome.reshape(17,15)
array([[ 7.07476433e-02, 4.76112748e+02, 9.46678098e+02,
3.82337005e+02, 1.93003218e+02, 4.90009755e+01,
6.00031395e+01, 8.07719653e+03, 7.08472528e+02,
6.66113297e+03, 2.10577024e+02, 7.03733127e+00,
2.64166778e+02, 8.81544288e+02, 2.80317529e+02],
[ 3.40636801e-02, 6.74108112e+02, 1.07549863e+03,
4.75613019e+02, 2.59586694e+02, 2.22334763e+02,
1.47100443e+02, 7.79192727e+03, 8.96827624e+02,
1.07574762e+04, 2.69159255e+02, 1.12322389e+01,
3.38886287e+02, 1.12661090e+03, 5.01499084e+02],
[ 2.74359052e-02, 8.76820248e+02, 1.27224495e+03,
7.72644899e+02, 2.52820795e+02, 2.17116682e+02,
2.26887020e+02, 8.37276002e+03, 6.79262724e+02,
6.66430540e+03, 3.03836865e+02, 3.89927750e+01,
3.70555945e+02, 2.25426578e+02, 7.92552177e+02],
[ 2.31201449e-02, 9.57398018e+02, 1.65313066e+03,
3.12674751e+02, 2.32042786e+02, 4.63582262e+01,
8.80367110e+01, 8.75564351e+03, 5.59566440e+02,
8.35537054e+03, 1.93387755e+02, 4.16661381e+01,
3.37049793e+02, 4.83547430e+02, 7.73972782e+02],
[ 3.32930086e-02, 9.69626708e+02, 1.29752911e+03,
9.77911912e+02, 2.59779096e+02, 6.71833599e+01,
8.97375530e+01, 8.85421422e+03, 6.75703910e+02,
8.29405271e+03, 2.38218996e+02, 1.17755401e+01,
3.24088091e+02, 3.10801546e+02, 6.80063699e+02],
[ 5.88793023e-02, 7.66412267e+02, 1.11808828e+03,
5.68988670e+02, 3.53606805e+02, 1.02089673e+02,
9.44373630e+01, 7.94101709e+03, 7.10640359e+02,
7.49075605e+03, 2.30317957e+02, 4.43470263e+00,
2.44390317e+02, 6.16475587e+02, 8.69942226e+02],
[ 1.01420369e-01, 1.08109566e+03, 1.55381901e+03,
4.95689697e+02, 2.55561483e+02, 1.54728923e+02,
5.91901759e+01, 8.75193836e+03, 5.85682205e+02,
8.36790873e+03, 2.12961123e+02, 8.98077448e+00,
3.10658917e+02, 4.89046639e+02, 6.24180927e+02],
[ 2.88231139e-02, 6.91240406e+02, 1.17208347e+03,
6.76462831e+02, 4.06588582e+02, 2.34986700e+02,
1.20913183e+02, 7.97004971e+03, 8.13971942e+02,
8.28287819e+03, 2.09747680e+02, 4.44975252e+00,
1.97324463e+02, 3.41816923e+02, 5.08186220e+02],
[ 2.48156222e-02, 8.13706783e+02, 1.08838375e+03,
6.14937527e+02, 3.53201238e+02, 8.10061070e+01,
7.10385961e+01, 7.35347555e+03, 3.97453108e+02,
9.02833249e+03, 1.86277880e+02, 6.77395809e+00,
3.17106377e+02, 2.01442633e+02, 7.40103340e+02],
[ 2.18870705e-02, 1.03721008e+03, 1.41128187e+03,
3.35109484e+02, 2.81539185e+02, 7.98645112e+01,
8.31708202e+01, 7.81739170e+03, 5.46414268e+02,
6.29833978e+03, 2.30595819e+02, 2.85566756e+01,
2.76767253e+02, 3.90489644e+02, 5.61430285e+02],
[ 2.82065767e-02, 6.51342645e+02, 1.18075914e+03,
1.44102060e+03, 2.67334673e+02, 1.38863439e+02,
1.17194400e+02, 8.12974831e+03, 4.14446128e+02,
7.86826552e+03, 2.34267416e+02, 2.95178623e+00,
1.87046167e+02, 1.21146267e+03, 6.30434303e+02],
[ 8.86272220e-02, 7.77016427e+02, 1.29332884e+03,
7.61269775e+02, 2.82519880e+02, 2.18647049e+02,
1.76553431e+02, 8.19587690e+03, 4.52575079e+02,
7.81030952e+03, 1.90375815e+02, 6.32898284e+00,
2.84079838e+02, 8.91924271e+02, 5.47441518e+02],
[ 2.06539961e-02, 6.61425510e+02, 9.83462567e+02,
6.28072889e+02, 1.93003218e+02, 1.48020924e+02,
1.41622947e+02, 7.10571648e+03, 1.71562336e+02,
6.68093460e+03, 1.78822268e+02, 5.47414876e+00,
1.83200154e+02, 2.37551761e+02, 6.17529937e+02],
[ 2.14246676e-02, 7.11453916e+02, 9.20990414e+02,
2.97181334e+02, 2.96385959e+02, 1.33816147e+02,
1.71932500e+02, 7.11578132e+03, 5.88948971e+02,
6.60041175e+03, 1.86477262e+02, 2.04327418e+00,
2.64922625e+02, 3.71917709e+02, 7.56622374e+02],
[ 2.95937854e-02, 8.57402464e+02, 1.29470753e+03,
2.20909884e+02, 2.14864352e+02, 1.74392236e+02,
1.31431768e+02, 7.03525418e+03, 5.55915840e+02,
8.90650178e+03, 1.76915413e+02, 1.19972753e+01,
3.36661245e+02, 3.98958064e+02, 9.34283322e+02],
[ 2.01915932e-02, 7.60193479e+02, 1.09996965e+03,
5.49725686e+02, 1.57682263e+02, 9.10534980e+01,
9.84716604e+01, 7.29515087e+03, 3.50450071e+02,
7.23445227e+03, 1.22316578e+02, 1.88239080e+01,
2.62661157e+02, 1.72461782e+02, 6.60146877e+02],
[ 1.12363904e-01, 6.35649273e+02, 1.12868067e+03,
7.81645362e+02, 1.97314956e+02, 2.29777608e+02,
9.69036038e+01, 5.93349007e+03, 5.96786704e+02,
5.53590589e+03, 1.69179818e+02, 7.72912481e+00,
4.27107822e+02, 6.41705644e+02, 4.60291865e+02]])
anbio = ar_annual_biome.T
anbio[0]
array([ 0.07074764, 0.03406368, 0.02743591, 0.02312014, 0.03329301,
0.0588793 , 0.10142037, 0.02882311, 0.02481562, 0.02188707,
0.02820658, 0.08862722, 0.020654 , 0.02142467, 0.02959379,
0.02019159, 0.1123639 ])
annual_biome
[0.0 0.070748 1.0 476.112748 2.0 946.678098 3.0 382.337005 4.0 193.003218 5.0 49.000975 6.0 60.003140 7.0 8077.196531 8.0 708.472528 9.0 6661.132970 10.0 210.577024 11.0 7.037331 12.0 264.166778 13.0 881.544288 14.0 280.317529 dtype: float64, 0.0 0.034064 1.0 674.108112 2.0 1075.498635 3.0 475.613019 4.0 259.586694 5.0 222.334763 6.0 147.100443 7.0 7791.927270 8.0 896.827624 9.0 10757.476237 10.0 269.159255 11.0 11.232239 12.0 338.886287 13.0 1126.610899 14.0 501.499084 dtype: float64, 0.0 0.027436 1.0 876.820248 2.0 1272.244951 3.0 772.644899 4.0 252.820795 5.0 217.116682 6.0 226.887020 7.0 8372.760020 8.0 679.262724 9.0 6664.305398 10.0 303.836865 11.0 38.992775 12.0 370.555945 13.0 225.426578 14.0 792.552177 dtype: float64, 0.0 0.023120 1.0 957.398018 2.0 1653.130656 3.0 312.674751 4.0 232.042786 5.0 46.358226 6.0 88.036711 7.0 8755.643506 8.0 559.566440 9.0 8355.370538 10.0 193.387755 11.0 41.666138 12.0 337.049793 13.0 483.547430 14.0 773.972782 dtype: float64, 0.0 0.033293 1.0 969.626708 2.0 1297.529114 3.0 977.911912 4.0 259.779096 5.0 67.183360 6.0 89.737553 7.0 8854.214220 8.0 675.703910 9.0 8294.052712 10.0 238.218996 11.0 11.775540 12.0 324.088091 13.0 310.801546 14.0 680.063699 dtype: float64, 0.0 0.058879 1.0 766.412267 2.0 1118.088281 3.0 568.988670 4.0 353.606805 5.0 102.089673 6.0 94.437363 7.0 7941.017089 8.0 710.640359 9.0 7490.756050 10.0 230.317957 11.0 4.434703 12.0 244.390317 13.0 616.475587 14.0 869.942226 dtype: float64, 0.0 0.101420 1.0 1081.095656 2.0 1553.819009 3.0 495.689697 4.0 255.561483 5.0 154.728923 6.0 59.190176 7.0 8751.938363 8.0 585.682205 9.0 8367.908731 10.0 212.961123 11.0 8.980774 12.0 310.658917 13.0 489.046639 14.0 624.180927 dtype: float64, 0.0 0.028823 1.0 691.240406 2.0 1172.083468 3.0 676.462831 4.0 406.588582 5.0 234.986700 6.0 120.913183 7.0 7970.049707 8.0 813.971942 9.0 8282.878189 10.0 209.747680 11.0 4.449753 12.0 197.324463 13.0 341.816923 14.0 508.186220 dtype: float64, 0.0 0.024816 1.0 813.706783 2.0 1088.383745 3.0 614.937527 4.0 353.201238 5.0 81.006107 6.0 71.038596 7.0 7353.475552 8.0 397.453108 9.0 9028.332491 10.0 186.277880 11.0 6.773958 12.0 317.106377 13.0 201.442633 14.0 740.103340 dtype: float64, 0.0 0.021887 1.0 1037.210079 2.0 1411.281867 3.0 335.109484 4.0 281.539185 5.0 79.864511 6.0 83.170820 7.0 7817.391705 8.0 546.414268 9.0 6298.339781 10.0 230.595819 11.0 28.556676 12.0 276.767253 13.0 390.489644 14.0 561.430285 dtype: float64, 0.0 0.028207 1.0 651.342645 2.0 1180.759136 3.0 1441.020598 4.0 267.334673 5.0 138.863439 6.0 117.194400 7.0 8129.748315 8.0 414.446128 9.0 7868.265518 10.0 234.267416 11.0 2.951786 12.0 187.046167 13.0 1211.462668 14.0 630.434303 dtype: float64, 0.0 0.088627 1.0 777.016427 2.0 1293.328843 3.0 761.269775 4.0 282.519880 5.0 218.647049 6.0 176.553431 7.0 8195.876899 8.0 452.575079 9.0 7810.309519 10.0 190.375815 11.0 6.328983 12.0 284.079838 13.0 891.924271 14.0 547.441518 dtype: float64, 0.0 0.020654 1.0 661.425510 2.0 983.462567 3.0 628.072889 4.0 193.003218 5.0 148.020924 6.0 141.622947 7.0 7105.716476 8.0 171.562336 9.0 6680.934603 10.0 178.822268 11.0 5.474149 12.0 183.200154 13.0 237.551761 14.0 617.529937 dtype: float64, 0.0 0.021425 1.0 711.453916 2.0 920.990414 3.0 297.181334 4.0 296.385959 5.0 133.816147 6.0 171.932500 7.0 7115.781325 8.0 588.948971 9.0 6600.411750 10.0 186.477262 11.0 2.043274 12.0 264.922625 13.0 371.917709 14.0 756.622374 dtype: float64, 0.0 0.029594 1.0 857.402464 2.0 1294.707535 3.0 220.909884 4.0 214.864352 5.0 174.392236 6.0 131.431768 7.0 7035.254183 8.0 555.915840 9.0 8906.501784 10.0 176.915413 11.0 11.997275 12.0 336.661245 13.0 398.958064 14.0 934.283322 dtype: float64, 0.0 0.020192 1.0 760.193479 2.0 1099.969645 3.0 549.725686 4.0 157.682263 5.0 91.053498 6.0 98.471660 7.0 7295.150872 8.0 350.450071 9.0 7234.452273 10.0 122.316578 11.0 18.823908 12.0 262.661157 13.0 172.461782 14.0 660.146877 dtype: float64, 0.0 0.112364 1.0 635.649273 2.0 1128.680668 3.0 781.645362 4.0 197.314956 5.0 229.777608 6.0 96.903604 7.0 5933.490065 8.0 596.786704 9.0 5535.905886 10.0 169.179818 11.0 7.729125 12.0 427.107822 13.0 641.705644 14.0 460.291865 dtype: float64]
fig, ax = plt.subplots(5,3, figsize =(18,12))
for i in range(5):
for j in range(3):
ax[i][j].plot(years, anbio[i*3 + j])
labels = ax[i][j].get_xticklabels()
plt.setp(labels, rotation=30, fontsize=10)
plt.show()
basket = 'BasketSemNomes.csv'
basket_df = pd.read_csv(basket,sep=';')
basket_df
| Ass.j | Roub.j | RD.j | RO.j | T.j | |
|---|---|---|---|---|---|
| 0 | 4.5 | 1.4 | 7.4 | 0.6 | 3.5 |
| 1 | 7.2 | 1.7 | 6.8 | 1.3 | 3.0 |
| 2 | 2.6 | 0.8 | 4.7 | 1.9 | 2.6 |
| 3 | 6.1 | 1.4 | 4.7 | 0.9 | 3.7 |
| 4 | 9.6 | 2.4 | 2.9 | 0.8 | 2.3 |
| 5 | 2.6 | 0.8 | 6.5 | 2.3 | 2.0 |
| 6 | 5.9 | 1.8 | 4.0 | 0.8 | 3.7 |
| 7 | 7.5 | 1.8 | 4.0 | 1.4 | 3.3 |
| 8 | 5.0 | 1.9 | 3.7 | 1.3 | 2.8 |
| 9 | 4.8 | 1.1 | 5.9 | 0.6 | 2.8 |
| 10 | 2.4 | 0.8 | 6.1 | 0.7 | 1.3 |
| 11 | 2.3 | 1.2 | 6.7 | 1.1 | 1.6 |
| 12 | 1.0 | 0.5 | 4.2 | 2.8 | 1.8 |
| 13 | 7.6 | 1.0 | 2.7 | 0.4 | 2.8 |
| 14 | 3.7 | 1.2 | 6.1 | 2.3 | 2.3 |
| 15 | 2.7 | 0.7 | 8.2 | 1.8 | 2.1 |
| 16 | 1.4 | 1.1 | 9.1 | 3.3 | 2.9 |
| 17 | 1.7 | 0.9 | 4.9 | 1.8 | 1.8 |
| 18 | 0.9 | 0.6 | 6.6 | 4.1 | 1.3 |
| 19 | 2.7 | 1.4 | 4.8 | 1.4 | 2.6 |
| 20 | 4.1 | 1.8 | 6.6 | 1.1 | 2.9 |
| 21 | 7.5 | 0.8 | 2.7 | 0.3 | 2.5 |
| 22 | 6.9 | 1.6 | 3.3 | 0.7 | 3.1 |
| 23 | 5.3 | 1.7 | 4.1 | 1.0 | 2.5 |
| 24 | 3.7 | 0.5 | 5.9 | 2.2 | 2.0 |
| 25 | 1.6 | 1.7 | 4.9 | 1.1 | 1.1 |
| 26 | 1.0 | 1.0 | 5.9 | 3.3 | 1.4 |
| 27 | 1.7 | 0.8 | 2.2 | 0.5 | 1.3 |
| 28 | 6.9 | 1.5 | 2.2 | 0.5 | 2.5 |
| 29 | 4.6 | 1.3 | 2.9 | 0.5 | 2.2 |
| 30 | 2.1 | 1.3 | 1.6 | 0.6 | 1.4 |
| 31 | 1.6 | 0.8 | 4.6 | 1.8 | 1.2 |
| 32 | 1.4 | 0.6 | 4.6 | 1.4 | 1.4 |
| 33 | 2.7 | 1.4 | 7.0 | 3.0 | 17.1 |
| 34 | 1.5 | 0.4 | 3.1 | 0.7 | 1.9 |
| 35 | 2.5 | 1.0 | 1.4 | 0.3 | 1.9 |
| 36 | 1.6 | 0.7 | 3.2 | 0.7 | 1.6 |
| 37 | 2.2 | 0.5 | 1.4 | 0.1 | 1.2 |
| 38 | 7.3 | 2.4 | 3.2 | 0.8 | 3.0 |
| 39 | 3.5 | 1.4 | 3.6 | 0.9 | 2.0 |
| 40 | 2.1 | 0.6 | 5.5 | 2.1 | 1.1 |
| 41 | 4.0 | 1.0 | 5.5 | 2.3 | 2.0 |
| 42 | 0.8 | 0.4 | 4.2 | 1.5 | 1.6 |
| 43 | 1.5 | 1.0 | 3.2 | 1.4 | 1.1 |
| 44 | 1.0 | 0.5 | 3.7 | 1.6 | 1.0 |
| 45 | 1.2 | 1.5 | 3.1 | 1.5 | 1.2 |
| 46 | 5.9 | 1.5 | 3.1 | 0.6 | 3.2 |
| 47 | 11.1 | 1.8 | 4.4 | 1.1 | 3.9 |
| 48 | 6.1 | 2.2 | 2.3 | 0.5 | 2.4 |
| 49 | 0.9 | 0.6 | 8.3 | 3.4 | 2.1 |
basket_df.describe()
| Ass.j | Roub.j | RD.j | RO.j | T.j | |
|---|---|---|---|---|---|
| count | 50.00000 | 50.000000 | 50.000000 | 50.000000 | 50.000000 |
| mean | 3.73000 | 1.176000 | 4.554000 | 1.382000 | 2.480000 |
| std | 2.55265 | 0.527435 | 1.863967 | 0.929711 | 2.251349 |
| min | 0.80000 | 0.400000 | 1.400000 | 0.100000 | 1.000000 |
| 25% | 1.60000 | 0.800000 | 3.125000 | 0.700000 | 1.450000 |
| 50% | 2.70000 | 1.100000 | 4.300000 | 1.100000 | 2.100000 |
| 75% | 5.75000 | 1.500000 | 5.900000 | 1.800000 | 2.800000 |
| max | 11.10000 | 2.400000 | 9.100000 | 4.100000 | 17.100000 |
!cat ITDcluster.r
# Import/Prepare Data
rm(list=ls())
graphics.off()
#Importing Data
#data(mtcars)
#mydata<-mtcars
#mydata <- read.table("C:/UTAD/InstTecnDesporto/2012_2013/TrabalhoAlunos/Basket_noheader.csv", header=FALSE,sep=";")
mydata <- read.table("C:/UTAD/InstTecnDesporto/2012_2013/TrabalhoAlunos/BasketSemNomes.csv", header=TRUE,sep=";");
mydata
#Sys.sleep(1)
sink(file="summaryBasket.txt")
summary(mydata)
sink(NULL)
mydata <- na.omit(mydata) # listwise deletion of missing
mydata <- scale(mydata) # standardize variables
#K-means clustering is the most popular partitioning method. It requires the analyst to specify the number of clusters to extract. A plot of the within groups sum of squares by number of clusters extracted can help determine the appropriate number of clusters. The analyst looks for a bend in the plot similar to a scree test in #factor analysis.
# Determine number of clusters
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(mydata,
centers=i)$withinss)
#jpeg("Plot_NumberOfClusters.jpg")
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
dev.copy(jpeg,filename="Plot_NumberOfClusters.jpg");
dev.off( )
#readline(prompt = "Pause. Press <Enter> to continue...")
# K-Means Cluster Analysis
fit <- kmeans(mydata, 5) # 5 cluster solution
# get cluster means
sink(file="ClusterMeans.txt")
aggregate(mydata,by=list(fit$cluster),FUN=mean)
sink(NULL)
# append cluster assignment
mydata <- data.frame(mydata, fit$cluster)
#A robust version of K-means based on mediods can be invoked by using pam( ) instead of kmeans( ). The function pamk( ) in the fpc package is a wrapper for pam that also prints the suggested number of clusters based on optimum average silhouette width.
#Hierarchical Agglomerative
#There are a wide range of hierarchical clustering approaches. I have had good luck with Ward's method described below.
############################################################################################################
# Ward Hierarchical Clustering
d <- dist(mydata, method = "euclidean") # distance matrix
fit <- hclust(d, method="ward")
#jpeg("Plot_WardHierarchicalClusteringDendogram.jpg")
plot(fit) # display dendogram
dev.copy(jpeg,filename="Plot_WardHierarchicalClusteringDendogram.jpg");
dev.off( )
groups <- cutree(fit, k=5) # cut tree into 5 clusters
# draw dendogram with red borders around the 5 clusters
rect.hclust(fit, k=5, border="red")
dev.copy(jpeg,filename="Plot_WardHierarchicalClusteringDendogram.jpg");
dev.off( )
#The pvclust( ) function in the pvclust package provides p-values for hierarchical clustering based on multiscale bootstrap resampling. Clusters that are highly supported by the data will have large p values. Interpretation details are provided Suzuki. Be aware that pvclust clusters columns, not rows. Transpose your data before using.
############################################################################################################
# Ward Hierarchical Clustering with Bootstrapped p values
library(pvclust)
fit <- pvclust(mydata, method.hclust="ward",
method.dist="euclidean")
#jpeg("Plot_WardHierarchicalClusteringDendogram_withBootstrapped_p_values.jpg")
plot(fit) # dendogram with p values
# add rectangles around groups highly supported by the data
pvrect(fit, alpha=.95)
dev.copy(jpeg,filename="Plot_WardHierarchicalClusteringDendogram_withBootstrapped_p_values.jpg");
dev.off( )
#Model Based
#Model based approaches assume a variety of data models and apply maximum likelihood estimation and Bayes criteria to identify the most likely model and number of clusters. Specifically, the Mclust( ) function in the mclust package selects the optimal model according to BIC for EM initialized by hierarchical clustering for parameterized Gaussian mixture models. (phew!). One chooses the model and number of clusters with the largest BIC. See help(mclustModelNames) to details on the model chosen as best.
# Model Based Clustering
#data(mtcars)
#mydata<-mtcars
#mydata <- read.table("C:/UTAD/InstTecnDesporto/2012_2013/TrabalhoAlunos/BasketComNomes.csv", header=TRUE,sep=";");
#mydata
library(mclust)
fit <- Mclust(mydata)
#jpeg("Plot_ModelBasedClusteringResults.jpg")
plot(fit) # plot results
dev.copy(jpeg,filename="Plot_ModelBasedClusteringResults.jpg");
dev.off( )
print(fit) # display the best model
#Plotting Cluster Solutions
#It is always a good idea to look at the cluster results.
# K-Means Clustering with 5 clusters
fit <- kmeans(mydata, 5)
# Cluster Plot against 1st 2 principal components
# vary parameters for most readable graph
library(cluster)
#jpeg("Plot_KMeansClusterplot.jpg")
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
dev.copy(jpeg,filename="Plot_KMeansClusterplot.jpg");
dev.off( )
# Centroid Plot against 1st 2 discriminant functions
library(fpc)
jpeg("Plot_CentroidPlot.jpg")
plotcluster(mydata, fit$cluster)
dev.off( )
#Validating cluster solutions
#The function cluster.stats() in the fpc package provides a mechanism for comparing the similarity of two cluster solutions using a variety of validation criteria (Hubert's gamma coefficient, the Dunn index and the corrected rand index)
# comparing 2 cluster solutions
#library(fpc)
#cluster.stats(d, fit1$cluster, fit2$cluster)
allpixels = np.load('../MODIS/MCD64A1/allpixels.npy')
allpixels[0]
20029079
annual_total[0]
20024630.0
an_total = np.array(annual_total[:-1])
diffs = (allpixels - an_total)*100/allpixels
diffs.max()
0.062423688066403825
fig, ax = plt.subplots(3,1)
ax[0].plot(years[:-1], annual_total[:-1])
ax[0].set_title("Pixeis havendo selecção p/ ecoregião")
ax[1].plot(years[:-1], allpixels)
ax[1].set_title("Pixeis não havendo selecção p/ ecoregião")
ax[2].plot(years[:-1], diffs)
ax[2].set_title("Diferença em percentagem %")
labels = [ax[i].get_xticklabels() for i in range(3)]
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout()
plt.show()
biome_names_dict = dict( (num, name) for num, name in zip(biome_nums, biome_names))
biome_names_dict
{0.0: 'N/A',
1.0: 'Tropical & Subtropical Moist Broadleaf Forests',
2.0: 'Tropical & Subtropical Dry Broadleaf Forests',
3.0: 'Tropical & Subtropical Coniferous Forests',
4.0: 'Temperate Broadleaf & Mixed Forests',
5.0: 'Temperate Conifer Forests',
6.0: 'Boreal Forests/Taiga',
7.0: 'Tropical & Subtropical Grasslands, Savannas & Shrublands',
8.0: 'Temperate Grasslands, Savannas & Shrublands',
9.0: 'Flooded Grasslands & Savannas',
10.0: 'Montane Grasslands & Shrublands',
11.0: 'Tundra',
12.0: 'Mediterranean Forests, Woodlands & Scrub',
13.0: 'Deserts & Xeric Shrublands',
14.0: 'Mangroves'}
fig, ax = plt.subplots(5,3, figsize =(18,12))
for i in range(5):
for j in range(3):
ax[i][j].plot(years, anbio[i*3 + j])
labels = ax[i][j].get_xticklabels()
ax[i][j].set_title(biome_names_dict[i*3 + j])
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout()
plt.show()
experimentar aplicar a escala das savanas às florestas mediterrânicas
savanas = 7; mediterraneo = 12
fig, ax = plt.subplots(2,1, sharey=True)
ax[0].plot(years, anbio[savanas])
labels = ax[0].get_xticklabels()
ax[0].set_title(biome_names_dict[savanas])
plt.setp(labels, rotation=30, fontsize=10)
ax[1].plot(years, anbio[mediterraneo])
labels = ax[1].get_xticklabels()
ax[1].set_title(biome_names_dict[mediterraneo])
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout()
plt.show()
fig, ax = plt.subplots(2,1, sharey=True)
ver também sharey='col' e sharey='row'
log_anbio = (np.log(anbio) - np.log(anbio.min()))/(np.log(anbio.max()) - np.log(anbio.min()))
fig, ax = plt.subplots(2,1, sharey=True)
fig.suptitle('escala logarítmica normalizada')
ax[0].plot(years, log_anbio[savanas])
labels = ax[0].get_xticklabels()
ax[0].set_title(biome_names_dict[savanas])
plt.setp(labels, rotation=30, fontsize=10)
ax[1].plot(years, log_anbio[mediterraneo])
labels = ax[1].get_xticklabels()
ax[1].set_title(biome_names_dict[mediterraneo])
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout()
plt.show()
log2_anbio = np.log(anbio -anbio.min() + 1) / np.log(anbio.max() - anbio.min() +1 )
fig, ax = plt.subplots(2,1, sharey=True)
fig.suptitle('escala logarítmica normalizada 2')
ax[0].plot(years, log2_anbio[savanas])
labels = ax[0].get_xticklabels()
ax[0].set_title(biome_names_dict[savanas])
plt.setp(labels, rotation=30, fontsize=10)
ax[1].plot(years, log2_anbio[mediterraneo])
labels = ax[1].get_xticklabels()
ax[1].set_title(biome_names_dict[mediterraneo])
plt.setp(labels, rotation=30, fontsize=10)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
x = np.linspace(.1,100000, 1000000)
log1 = np.log(x - x.min() +1) /np.log(x.max() - x.min() +1)
log2 = (np.log(x) - np.log(x.min()))/(np.log(x.max()) - np.log(x.min()))
fig, ax = plt.subplots(2,1, sharey=True)
fig.suptitle('escalas logarítmicas normalizadas')
ax[0].plot(x, log2, 'b')
ax[0].set_title(r"$\frac{\log(x) - \log(x_{min})} {\log(x_{max}) - \log(x_{min})}$" )
ax[1].plot(x, log1, 'r')
ax[1].set_title(r"$\frac{\log(x - x_{min} +1)}{ \log(x_{max}- x_{min} +1)}$")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
fig, ax = plt.subplots(2,1, sharey=True,sharex=True )
labels = years[::2]
ax[0].plot(years, anbio[savanas])
ax[0].set_title(biome_names_dict[savanas])
ax[1].plot(years, anbio[mediterraneo])
ax[1].set_title(biome_names_dict[mediterraneo])
plt.xticks(labels,labels)
fig.tight_layout()
plt.show()
fig, ax = plt.subplots(2,1, sharey=True)
labels = years[::2]
#plt.setp(ax, xticks=labels, xticklabels=labels)
ax[0].plot(years, anbio[savanas])
ax[0].set_title(biome_names_dict[savanas])
ax[0].set_xticks(labels)
ax[0].set_xticklabels(labels)
ax[1].plot(years, anbio[mediterraneo])
ax[1].set_title(biome_names_dict[mediterraneo])
ax[1].set_xticks(labels)
ax[1].set_xticklabels(labels)
fig.tight_layout()
plt.show()
from scipy import stats
years_float = np.array([float(yr) for yr in years])
fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = years[::2]
for i in range(5):
for j in range(3):
slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, anbio[i*3 + j])
ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i][j].plot(years, anbio[i*3 + j], 'o')
ax[i][j].set_xticks(labels)
ax[i][j].set_xticklabels(labels)
ax[i][j].set_title(biome_names_dict[i*3 + j])
ax[i][j].legend()
fig.tight_layout()
plt.show()
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html
import seaborn as sns; sns.set()
fig, ax = plt.subplots(2,1, sharey=True)
labels = years[::2]
#plt.setp(ax, xticks=labels, xticklabels=labels)
ax[0].plot(years, anbio[savanas])
ax[0].set_title(biome_names_dict[savanas])
ax[0].set_xticks(labels)
ax[0].set_xticklabels(labels)
ax[1].plot(years, anbio[mediterraneo])
ax[1].set_title(biome_names_dict[mediterraneo])
ax[1].set_xticks(labels)
ax[1].set_xticklabels(labels)
fig.tight_layout()
plt.show()
fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = years[::2]
for i in range(5):
for j in range(3):
slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, anbio[i*3 + j])
ax[i][j].plot(years, intercept + slope*years_float, 'r', label="r-squared: {} ".format(r_value**2))
ax[i][j].plot(years, anbio[i*3 + j], 'o')
ax[i][j].set_xticks(labels)
ax[i][j].set_xticklabels(labels)
ax[i][j].set_title(biome_names_dict[i*3 + j])
ax[i][j].legend()
fig.tight_layout()
plt.show()
%%HTML
<h1 id="1">PLOTS<h1>
fig, ax = plt.subplots()
labels = years[::2]
ax.plot(years, annual_total)
ax.set_xticks(labels)
ax.set_xticklabels(labels, fontsize=15)
ax.set_title('Interannual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)
#plt.setp(labels_x,fontsize=15, family='Times New Roman')
plt.setp(labels_y,fontsize=15)
#plt.xlabel(r"Burned Pixels", fontsize = 12)
plt.ylabel(r"Burned Pixels", fontsize = 15)
plt.tight_layout()
plt.show()
#help(off)
months = 'Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec'.split(' ')
fig, ax = plt.subplots()
ax.plot( np.arange(12),monthly_total)
ax.set_xticks(np.arange(12))
ax.set_xticklabels(months, fontsize=15)
ax.set_title('Intra-annual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)
plt.setp(labels_y,fontsize=15)
plt.ylabel(r"Burned Pixels", fontsize = 15)
plt.tight_layout()
plt.show()
residuals_monthly_total = (np.array(monthly_total) - mean(monthly_total))/mean(monthly_total)
fig, ax = plt.subplots()
ax.plot( np.arange(12),residuals_monthly_total)
ax.set_xticks(np.arange(12))
ax.set_xticklabels(months, fontsize=15)
ax.set_title('Intra-annual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)
plt.setp(labels_y,fontsize=15)
plt.ylabel(r"$\frac{Burned Pixels - \langle Burned Pixels \rangle}{\langle Burned Pixels \rangle}$", fontsize = 15)
plt.tight_layout()
plt.show()
130070.096/3600
36.13058222222222
$\frac{Burned Pixels - \langle Burned Pixels \rangle}{\langle Burned Pixels \rangle}$
ym = itertools.product(years,months)
ymlabels = [ m + ' ' + y[-2:] for y, m in ym]
fig, ax = plt.subplots(figsize=(16,12))
labels = ymlabels[::6]
ax.plot( np.arange(12*17),all_months)
ax.axhline(y=mean(all_months), color='r', linestyle='--', label='average')
ax.set_xticks(np.arange(0,12*17,6))
ax.set_xticklabels(labels,fontsize=12, rotation=30)
labels_y = ax.get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax.set_title('Intra-monthly Variability',fontsize=15)
plt.legend()
plt.show()
fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = years[::2]
for i in range(5):
for j in range(3):
slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, anbio[i*3 + j])
ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i][j].plot(years, anbio[i*3 + j], 'o')
ax[i][j].set_xticks(labels)
ax[i][j].set_xticklabels(labels,fontsize=15)
labels_y = ax[i][j].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i][j].set_title(biome_names_dict[i*3 + j],fontsize=15)
ax[i][j].legend(fontsize=15)
fig.tight_layout()
plt.show()
%%HTML
<h1 id="varintrabio"> Variação intra_anual em cada bioma</h1>
len(monthly_biome[0])
15
ar_monthly_biome = np.array(monthly_biome)
ar_monthly_biome.shape
(12, 15)
monbio = ar_monthly_biome.T
monbio.shape
(15, 12)
fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = months
for i in range(5):
for j in range(3):
#slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
#ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i][j].plot(months, monbio[i*3 + j], 'o')
ax[i][j].set_xticks(np.arange(12))
ax[i][j].set_xticklabels(labels,fontsize=15)
labels_y = ax[i][j].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i][j].set_title(biome_names_dict[i*3 + j],fontsize=15)
ax[i][j].legend(fontsize=15)
fig.tight_layout()
plt.show()
fig, ax = plt.subplots(5,3, figsize =(18,12) )
labels = months
for i in range(5):
for j in range(3):
#slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
#ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i][j].plot(months, monbio[i*3 + j], 'o')
ax[i][j].set_xticks(np.arange(12))
ax[i][j].set_xticklabels(labels,fontsize=15)
labels_y = ax[i][j].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i][j].set_title(biome_names_dict[i*3 + j],fontsize=15)
ax[i][j].legend(fontsize=15)
fig.tight_layout()
plt.show()
type(df_finall)
pandas.core.frame.DataFrame
biome_grouped.sum()/areas_biome
| BIOME_NUM | 0.0 | 1.0 | 2.0 | 3.0 | 4.0 | 5.0 | 6.0 | 7.0 | 8.0 | 9.0 | 10.0 | 11.0 | 12.0 | 13.0 | 14.0 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2001 | Jan | 0.005549 | 27.050378 | 294.159587 | 2.457691 | 0.708703 | 0.235960 | 0.054303 | 816.822779 | 136.908757 | 1317.788732 | 8.662509 | 0.000000 | 87.917982 | 150.205357 | 6.904017 |
| Fev | 0.000000 | 59.446635 | 130.801451 | 8.435858 | 0.742616 | 0.208993 | 0.000000 | 317.193617 | 20.659474 | 510.126430 | 8.036789 | 0.000000 | 11.783918 | 6.511449 | 62.714495 | |
| Mar | 0.000000 | 44.769512 | 57.736912 | 34.308037 | 4.959537 | 1.622504 | 0.003567 | 145.483777 | 15.521933 | 320.159545 | 3.064967 | 0.000000 | 13.559701 | 9.062745 | 85.992960 | |
| Abr | 0.000000 | 23.675130 | 52.096810 | 137.597482 | 21.573224 | 6.919239 | 2.560974 | 132.973701 | 42.203437 | 289.126569 | 0.702079 | 0.000000 | 20.769079 | 42.457831 | 64.666416 | |
| Mai | 0.000000 | 9.948100 | 58.293280 | 194.406676 | 1.519144 | 2.179819 | 1.100731 | 360.944364 | 11.273052 | 95.712628 | 6.766259 | 0.000000 | 2.653051 | 42.684068 | 25.989465 | |
| Jun | 0.012331 | 46.332785 | 43.506737 | 3.371022 | 7.151395 | 3.089949 | 4.647092 | 726.834858 | 40.303456 | 405.066977 | 49.474295 | 0.206685 | 14.910511 | 37.437618 | 2.385681 | |
| Jul | 0.025586 | 27.403217 | 23.248225 | 0.016606 | 25.104973 | 4.184353 | 32.171323 | 530.560284 | 161.060291 | 787.093604 | 26.263268 | 3.521676 | 20.975495 | 42.202259 | 2.349534 | |
| Ago | 0.021425 | 82.596794 | 94.099520 | 0.000000 | 50.715174 | 17.067755 | 17.046473 | 1240.009087 | 187.251154 | 1231.583918 | 54.378242 | 3.269339 | 35.227296 | 119.378443 | 10.590978 | |
| Set | 0.002774 | 60.699336 | 81.425340 | 0.531393 | 34.585260 | 10.092336 | 2.031418 | 1041.333825 | 59.118856 | 602.505641 | 41.957172 | 0.028093 | 30.625437 | 147.242173 | 5.385856 | |
| Out | 0.001387 | 33.984645 | 63.303647 | 0.249090 | 27.503074 | 2.988824 | 0.386861 | 815.557296 | 13.036188 | 485.267852 | 7.249867 | 0.011538 | 16.470772 | 85.292188 | 7.373923 | |
| Nov | 0.001695 | 32.765022 | 30.459607 | 0.730665 | 5.721531 | 0.242701 | 0.000396 | 800.815227 | 2.544155 | 123.222787 | 0.434822 | 0.000000 | 5.075402 | 103.090578 | 2.927881 | |
| Dez | 0.000000 | 27.441196 | 17.546983 | 0.232484 | 12.718589 | 0.168543 | 0.000000 | 1148.667716 | 18.591775 | 493.478286 | 3.586754 | 0.000000 | 4.198134 | 95.979580 | 3.036321 | |
| 2002 | Jan | 0.000000 | 38.795751 | 314.488409 | 2.457691 | 10.087252 | 0.020225 | 0.000000 | 772.452931 | 17.127363 | 1716.037356 | 5.985700 | 0.000000 | 21.373149 | 71.646836 | 30.182481 |
| Fev | 0.003391 | 42.650640 | 112.967112 | 3.819385 | 3.579504 | 2.546118 | 0.001189 | 357.576610 | 21.891082 | 1204.121108 | 5.519062 | 0.000000 | 15.751352 | 21.735638 | 72.835567 | |
| Mar | 0.000617 | 43.605021 | 71.166163 | 42.528017 | 34.784582 | 13.964323 | 1.120946 | 153.289352 | 17.100661 | 591.558397 | 2.814679 | 0.000000 | 15.317272 | 14.328885 | 77.064729 | |
| Abr | 0.000000 | 32.785237 | 50.562214 | 98.440487 | 14.291024 | 10.946285 | 3.412783 | 144.868016 | 23.242847 | 426.961466 | 2.555907 | 0.000000 | 18.349764 | 46.175674 | 43.448313 | |
| Mai | 0.000154 | 21.687960 | 48.083014 | 259.369427 | 17.277404 | 18.759924 | 7.086776 | 436.149574 | 22.342505 | 332.981836 | 26.929288 | 0.170064 | 5.497340 | 34.714736 | 44.677301 | |
| Jun | 0.005086 | 18.960196 | 23.932985 | 53.238900 | 17.788168 | 41.270484 | 19.215829 | 479.351371 | 30.773513 | 481.091611 | 22.337141 | 0.892459 | 17.296436 | 15.433147 | 6.940163 | |
| Jul | 0.011868 | 40.711863 | 40.098220 | 13.185180 | 24.835749 | 36.196226 | 57.357341 | 814.174340 | 111.809319 | 883.222909 | 69.032815 | 1.341447 | 33.026539 | 72.953225 | 16.193714 | |
| Ago | 0.008632 | 116.581438 | 106.217332 | 1.129209 | 54.015764 | 34.587205 | 46.529981 | 1027.067217 | 222.770864 | 974.996044 | 52.233826 | 8.248848 | 23.753002 | 190.515342 | 51.545170 | |
| Set | 0.002158 | 134.690165 | 92.635233 | 0.033212 | 46.525244 | 56.965179 | 11.333530 | 846.622593 | 330.111849 | 554.654062 | 54.346426 | 0.579421 | 21.309403 | 127.365858 | 57.979280 | |
| Out | 0.000925 | 92.179191 | 123.950790 | 0.182666 | 18.431815 | 3.498946 | 0.925138 | 741.187762 | 74.974558 | 524.568080 | 16.578396 | 0.000000 | 30.959345 | 244.736296 | 73.305474 | |
| Nov | 0.000000 | 42.109743 | 54.890877 | 0.780483 | 12.106087 | 3.285459 | 0.116930 | 684.890193 | 18.111982 | 972.666440 | 6.429007 | 0.000000 | 63.445560 | 226.732565 | 11.458499 | |
| Dez | 0.001233 | 49.350906 | 36.506285 | 0.448363 | 5.864102 | 0.294388 | 0.000000 | 1334.297312 | 6.571080 | 2094.616927 | 4.397008 | 0.000000 | 72.807126 | 60.272696 | 15.868394 | |
| 2003 | Jan | 0.002929 | 69.941757 | 263.764176 | 1.942904 | 27.791677 | 1.289913 | 0.000000 | 998.604149 | 24.766503 | 735.198363 | 69.471880 | 0.000000 | 37.652683 | 16.461863 | 54.364612 |
| Fev | 0.000000 | 50.174197 | 229.266318 | 8.286404 | 8.082951 | 1.269688 | 0.008324 | 377.099455 | 21.467196 | 400.701337 | 18.175573 | 0.000000 | 46.558920 | 4.743344 | 66.039990 | |
| Mar | 0.000308 | 141.255666 | 131.024609 | 156.827253 | 45.210960 | 25.045442 | 31.788821 | 174.533097 | 32.840379 | 586.880249 | 4.503062 | 0.318556 | 28.200050 | 8.927324 | 230.182079 | |
| Abr | 0.002620 | 68.361945 | 64.064831 | 141.184382 | 39.548257 | 7.528240 | 12.111613 | 118.742724 | 74.783476 | 536.263449 | 3.637660 | 0.024080 | 9.224968 | 12.533053 | 172.347386 | |
| Mai | 0.000617 | 53.719126 | 100.271534 | 371.842000 | 42.043943 | 91.251255 | 65.871461 | 489.978533 | 116.057365 | 307.128915 | 22.721057 | 1.783413 | 6.914932 | 14.200697 | 126.585685 | |
| Jun | 0.001541 | 29.766503 | 26.632286 | 85.238701 | 27.395108 | 13.573304 | 39.553794 | 516.877145 | 24.638837 | 435.361298 | 22.731662 | 4.474334 | 22.478081 | 7.319955 | 21.037370 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2015 | Jul | 0.009865 | 49.177549 | 28.506206 | 3.121932 | 11.771806 | 29.641040 | 51.616652 | 877.116669 | 55.152845 | 834.954653 | 26.581431 | 3.372682 | 29.053033 | 22.235128 | 19.699942 |
| Ago | 0.003545 | 75.837109 | 81.257207 | 0.714059 | 17.249720 | 81.147683 | 14.210811 | 1004.760086 | 138.258019 | 645.641193 | 31.228727 | 4.995562 | 25.128096 | 44.712165 | 43.845927 | |
| Set | 0.010481 | 117.677322 | 155.248006 | 0.016606 | 43.558242 | 19.568929 | 3.762781 | 932.665415 | 63.716526 | 400.275190 | 23.022251 | 0.428420 | 37.601079 | 25.200723 | 126.260365 | |
| Out | 0.001079 | 98.924786 | 122.880852 | 0.066424 | 37.222133 | 3.355123 | 0.590598 | 558.773372 | 42.068260 | 398.447493 | 20.385743 | 0.135951 | 28.330578 | 99.578076 | 85.089292 | |
| Nov | 0.001541 | 68.564705 | 84.552616 | 0.464969 | 13.839779 | 0.793274 | 0.000000 | 727.556957 | 9.015938 | 1035.272177 | 8.745231 | 0.000000 | 57.073988 | 91.097199 | 14.747847 | |
| Dez | 0.000000 | 63.341340 | 144.420233 | 1.345088 | 1.745458 | 1.155079 | 0.000000 | 806.393262 | 3.884238 | 1914.820751 | 5.898736 | 0.000000 | 7.066708 | 18.853225 | 32.387428 | |
| 2016 | Jan | 0.000000 | 83.399258 | 207.277563 | 3.786173 | 4.299972 | 0.444953 | 0.000000 | 992.973780 | 3.777432 | 1048.236517 | 5.807529 | 0.000000 | 30.476696 | 9.644612 | 38.532364 |
| Fev | 0.000000 | 94.105716 | 147.737041 | 17.868078 | 5.158859 | 1.487670 | 0.000000 | 290.776033 | 9.225379 | 373.768844 | 2.536817 | 0.000000 | 12.211927 | 4.665387 | 102.403554 | |
| Mar | 0.000000 | 86.784919 | 87.951962 | 32.514587 | 17.540399 | 8.584441 | 1.627513 | 102.840953 | 29.753014 | 152.816333 | 1.628993 | 0.000502 | 11.213238 | 6.606284 | 151.816070 | |
| Abr | 0.000000 | 93.673856 | 107.751929 | 267.655831 | 24.626737 | 10.206945 | 3.311311 | 130.202221 | 56.154986 | 243.812933 | 1.022363 | 0.006522 | 9.100511 | 16.272997 | 144.478293 | |
| Mai | 0.000000 | 34.980680 | 67.895209 | 157.275616 | 20.716413 | 20.085793 | 15.270716 | 270.625179 | 35.902710 | 334.165578 | 14.576092 | 0.365211 | 5.454843 | 17.217326 | 86.065253 | |
| Jun | 0.002158 | 22.538817 | 27.796990 | 60.528943 | 3.630027 | 13.440717 | 13.304699 | 585.744361 | 13.601092 | 758.920549 | 16.287808 | 2.212335 | 24.126372 | 3.559517 | 25.953319 | |
| Jul | 0.006628 | 50.751236 | 51.726918 | 1.295270 | 8.793731 | 6.872047 | 39.127295 | 784.563820 | 49.426201 | 614.863906 | 18.928558 | 7.814909 | 19.582188 | 9.605633 | 14.422527 | |
| Ago | 0.010173 | 78.819089 | 110.866977 | 3.653324 | 19.042240 | 16.083466 | 5.492955 | 961.328354 | 62.429846 | 467.663244 | 28.235876 | 8.049688 | 32.039992 | 16.054797 | 31.989815 | |
| Set | 0.000771 | 83.842144 | 86.081099 | 0.415151 | 24.799760 | 8.445112 | 19.959427 | 778.828782 | 33.844189 | 541.642372 | 18.355865 | 0.367719 | 30.786320 | 9.806956 | 11.386205 | |
| Out | 0.000308 | 61.889555 | 118.069188 | 0.315514 | 18.074003 | 3.098938 | 0.199773 | 642.144371 | 7.717577 | 592.751609 | 7.661357 | 0.007023 | 20.829790 | 20.329995 | 16.410594 | |
| Nov | 0.000154 | 36.569680 | 65.409896 | 1.378300 | 7.731368 | 0.961817 | 0.177972 | 744.282711 | 5.754180 | 735.075254 | 3.260107 | 0.000000 | 25.817161 | 46.270509 | 13.771886 | |
| Dez | 0.000000 | 32.838530 | 21.404874 | 3.038902 | 3.268754 | 1.341600 | 0.000000 | 1010.840306 | 42.863465 | 1370.735136 | 4.015213 | 0.000000 | 41.022118 | 12.427770 | 22.916997 | |
| 2017 | Jan | 0.000462 | 28.227121 | 85.338256 | 21.471584 | 13.039720 | 0.961817 | 0.000000 | 504.634307 | 48.590944 | 988.850558 | 5.652690 | 0.000000 | 27.574732 | 2.441994 | 29.061933 |
| Fev | 0.002620 | 76.634060 | 324.906548 | 24.942242 | 5.839187 | 3.301189 | 0.072140 | 273.661670 | 18.064419 | 526.282139 | 2.203807 | 0.000000 | 14.904440 | 5.218321 | 74.389874 | |
| Mar | 0.000154 | 94.194538 | 140.553171 | 79.526230 | 45.869832 | 10.694595 | 3.043362 | 111.447128 | 58.855179 | 548.839522 | 2.218654 | 0.049163 | 56.041908 | 7.587582 | 131.031727 | |
| Abr | 0.000308 | 60.064715 | 157.430679 | 259.585305 | 56.259529 | 0.991031 | 9.359982 | 97.633710 | 67.168533 | 379.753842 | 1.677778 | 0.003010 | 46.762301 | 11.181658 | 108.620783 | |
| Mai | 0.000000 | 16.789256 | 54.548497 | 202.360960 | 6.187309 | 2.905676 | 2.308484 | 323.876235 | 23.780216 | 126.234227 | 10.959643 | 0.007023 | 29.593357 | 31.725031 | 39.508325 | |
| Jun | 0.026049 | 16.990791 | 24.217283 | 191.616865 | 12.124773 | 13.229477 | 10.505900 | 572.259980 | 34.535091 | 345.463210 | 20.595730 | 1.250646 | 38.171758 | 36.515390 | 17.928755 | |
| Jul | 0.015876 | 44.176546 | 27.524920 | 0.813695 | 6.546505 | 57.333726 | 25.785733 | 718.447484 | 101.440781 | 719.885479 | 26.687485 | 5.416959 | 48.623079 | 102.125755 | 27.543773 | |
| Ago | 0.015568 | 64.544423 | 54.050211 | 0.631029 | 11.328866 | 88.520863 | 40.606564 | 884.540316 | 113.463145 | 663.113222 | 36.071162 | 0.864867 | 44.813492 | 75.250154 | 8.530617 | |
| Set | 0.038688 | 135.997998 | 130.547723 | 0.033212 | 14.984500 | 44.758194 | 5.097770 | 1082.794121 | 100.344350 | 588.764766 | 25.892078 | 0.137456 | 18.040140 | 134.035231 | 8.133004 | |
| Out | 0.012485 | 63.255581 | 88.040614 | 0.365332 | 17.111302 | 3.964124 | 0.120894 | 723.049544 | 23.725978 | 368.456211 | 28.800085 | 0.000000 | 66.311098 | 105.064426 | 7.554657 | |
| Nov | 0.000154 | 34.774244 | 41.522766 | 0.298908 | 8.023431 | 3.116916 | 0.002775 | 641.145570 | 6.818069 | 280.262711 | 8.420705 | 0.000000 | 36.271518 | 130.560101 | 7.988417 | |
| Dez | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
204 rows × 15 columns
biome_grouped.sum()[14][16*12]/areas_biome[14]
29.061933349349864
biomes_pa = biome_grouped.sum()/areas_biome
biomes_pa.index.get_level_values(level=1)
Index(['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out',
...
'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez'],
dtype='object', length=204)
biomes_pa.index.get_level_values(level=1)
Index(['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out',
...
'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez'],
dtype='object', length=204)
intra_biome = biomes_pa.groupby(biomes_pa.index.get_level_values(level=1), sort=False).sum()
for a, b in biomes_pa.groupby(biomes_pa.index.get_level_values(level=1),sort=False):
print(a)
print(b)
Jan BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Jan 0.005549 27.050378 294.159587 2.457691 0.708703 0.235960 2002 Jan 0.000000 38.795751 314.488409 2.457691 10.087252 0.020225 2003 Jan 0.002929 69.941757 263.764176 1.942904 27.791677 1.289913 2004 Jan 0.000000 46.164329 356.735676 5.131260 1.690783 0.038203 2005 Jan 0.000000 81.842110 281.295875 7.339861 1.352349 0.379783 2006 Jan 0.004316 33.638544 229.140982 9.664704 6.664161 2.114649 2007 Jan 0.001387 59.109110 429.797157 2.457691 8.703067 1.114629 2008 Jan 0.000308 51.483254 265.298773 13.816209 0.824975 0.159554 2009 Jan 0.007861 38.517033 158.662637 6.293682 2.587043 1.373061 2010 Jan 0.000000 38.863134 300.401911 2.424479 1.015992 1.116876 2011 Jan 0.000154 33.209746 333.621958 4.931988 0.574437 0.858444 2012 Jan 0.000000 37.056059 265.622811 2.025934 2.193934 0.707879 2013 Jan 0.000462 49.828096 235.612579 4.965200 6.802580 0.979795 2014 Jan 0.000000 33.885409 170.587860 1.262058 8.274661 0.955075 2015 Jan 0.000000 55.159273 272.210573 2.291631 2.052055 1.155079 2016 Jan 0.000000 83.399258 207.277563 3.786173 4.299972 0.444953 2017 Jan 0.000462 28.227121 85.338256 21.471584 13.039720 0.961817 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Jan 0.054303 816.822779 136.908757 1317.788732 8.662509 0.0 2002 Jan 0.000000 772.452931 17.127363 1716.037356 5.985700 0.0 2003 Jan 0.000000 998.604149 24.766503 735.198363 69.471880 0.0 2004 Jan 0.000000 667.036472 6.575252 1290.950938 9.954249 0.0 2005 Jan 0.000000 1054.433495 7.387145 1131.155263 9.205507 0.0 2006 Jan 0.094337 703.938134 9.546631 1314.815173 14.864560 0.0 2007 Jan 0.000000 877.533672 2.635942 1228.657709 12.928010 0.0 2008 Jan 0.000000 840.320232 3.999389 1122.035716 5.707838 0.0 2009 Jan 0.000000 622.330905 9.943817 1177.311724 7.705900 0.0 2010 Jan 0.000000 770.405220 2.907129 932.210880 7.616814 0.0 2011 Jan 0.000000 722.594126 4.052792 1213.089136 9.807895 0.0 2012 Jan 0.000000 820.254560 7.760967 1352.808550 8.598876 0.0 2013 Jan 0.000000 849.253217 8.885769 1189.944617 6.095997 0.0 2014 Jan 0.007927 694.905492 29.568607 1221.185930 4.950611 0.0 2015 Jan 0.000000 674.351554 13.415016 1140.691487 6.643237 0.0 2016 Jan 0.000000 992.973780 3.777432 1048.236517 5.807529 0.0 2017 Jan 0.000000 504.634307 48.590944 988.850558 5.652690 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Jan 87.917982 150.205357 6.904017 2002 Jan 21.373149 71.646836 30.182481 2003 Jan 37.652683 16.461863 54.364612 2004 Jan 4.295271 9.402703 19.989116 2005 Jan 42.151334 11.301005 100.668513 2006 Jan 20.310714 1.830793 15.109314 2007 Jan 8.769639 5.669590 39.580618 2008 Jan 21.600813 30.440342 46.340048 2009 Jan 21.464215 4.134955 34.773109 2010 Jan 23.294638 5.230377 25.555705 2011 Jan 5.706792 12.747235 29.170373 2012 Jan 16.276498 40.269399 40.195112 2013 Jan 7.804341 30.202451 16.808208 2014 Jan 58.476401 8.624335 14.711700 2015 Jan 11.122173 11.515187 69.112458 2016 Jan 30.476696 9.644612 38.532364 2017 Jan 27.574732 2.441994 29.061933 Fev BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Fev 0.000000 59.446635 130.801451 8.435858 0.742616 0.208993 2002 Fev 0.003391 42.650640 112.967112 3.819385 3.579504 2.546118 2003 Fev 0.000000 50.174197 229.266318 8.286404 8.082951 1.269688 2004 Fev 0.000000 113.045085 178.939490 8.136950 3.245915 0.921367 2005 Fev 0.001387 107.077450 143.643764 16.340324 2.214697 1.806778 2006 Fev 0.000000 52.210985 265.097013 21.886734 3.701312 1.543851 2007 Fev 0.002158 110.519468 119.723007 5.197684 5.190696 2.851742 2008 Fev 0.002312 74.455769 164.935530 6.559378 17.835231 3.591083 2009 Fev 0.000000 120.912905 306.726773 42.710683 12.481893 2.791067 2010 Fev 0.000000 94.073863 79.355774 7.638769 2.551746 2.905676 2011 Fev 0.000771 49.249832 149.883031 14.380813 4.991373 0.723610 2012 Fev 0.002004 104.831164 157.024103 18.067350 6.722297 1.182046 2013 Fev 0.002774 71.312684 160.668006 15.410387 4.876486 2.449487 2014 Fev 0.000154 68.994115 188.003394 4.201323 15.160292 2.487690 2015 Fev 0.000000 61.403788 179.355238 8.469070 10.694910 1.022492 2016 Fev 0.000000 94.105716 147.737041 17.868078 5.158859 1.487670 2017 Fev 0.002620 76.634060 324.906548 24.942242 5.839187 3.301189 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Fev 0.000000 317.193617 20.659474 510.126430 8.036789 0.0 2002 Fev 0.001189 357.576610 21.891082 1204.121108 5.519062 0.0 2003 Fev 0.008324 377.099455 21.467196 400.701337 18.175573 0.0 2004 Fev 0.000000 479.696553 4.898896 776.989184 7.016547 0.0 2005 Fev 0.046772 365.888823 11.448281 536.149810 5.930552 0.0 2006 Fev 0.000396 266.026460 7.617446 618.604530 7.642267 0.0 2007 Fev 0.000000 470.997401 7.161851 1031.948230 13.456160 0.0 2008 Fev 0.042808 379.819157 9.573333 987.373248 7.472581 0.0 2009 Fev 0.000000 257.423068 6.117154 777.737309 10.439978 0.0 2010 Fev 0.077689 308.803459 3.132423 380.615606 6.129934 0.0 2011 Fev 0.000000 394.403668 10.406921 1092.981957 5.190294 0.0 2012 Fev 0.001189 390.678482 4.648569 893.109520 5.597542 0.0 2013 Fev 0.000000 343.137410 5.544740 604.304929 5.018486 0.0 2014 Fev 0.005946 360.093100 6.239814 714.430797 2.948307 0.0 2015 Fev 0.017837 308.463287 3.942648 920.098834 6.204172 0.0 2016 Fev 0.000000 290.776033 9.225379 373.768844 2.536817 0.0 2017 Fev 0.072140 273.661670 18.064419 526.282139 2.203807 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Fev 11.783918 6.511449 62.714495 2002 Fev 15.751352 21.735638 72.835567 2003 Fev 46.558920 4.743344 66.039990 2004 Fev 8.642147 3.420480 124.163857 2005 Fev 47.172097 11.537288 153.514964 2006 Fev 11.908374 4.948685 96.692378 2007 Fev 9.695475 22.647016 134.718689 2008 Fev 8.733213 6.416213 98.246685 2009 Fev 10.047596 3.844021 104.897675 2010 Fev 69.862665 7.766402 128.248432 2011 Fev 6.274435 14.488818 109.018397 2012 Fev 13.034555 36.203963 89.173868 2013 Fev 8.241457 7.104568 80.173343 2014 Fev 5.907136 7.041881 129.079806 2015 Fev 46.901935 6.745321 98.318978 2016 Fev 12.211927 4.665387 102.403554 2017 Fev 14.904440 5.218321 74.389874 Mar BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Mar 0.000000 44.769512 57.736912 34.308037 4.959537 1.622504 2002 Mar 0.000617 43.605021 71.166163 42.528017 34.784582 13.964323 2003 Mar 0.000308 141.255666 131.024609 156.827253 45.210960 25.045442 2004 Mar 0.000308 220.293442 180.354866 84.740520 8.633857 3.462990 2005 Mar 0.000925 104.012774 116.048534 96.680248 19.184811 7.764200 2006 Mar 0.000154 128.668012 70.255187 87.596756 13.741502 5.892252 2007 Mar 0.000771 205.113401 105.202419 32.547799 30.962846 6.795641 2008 Mar 0.000771 103.620118 127.454073 55.862651 80.434329 68.792381 2009 Mar 0.001541 125.883892 141.947148 52.358781 26.440020 9.977727 2010 Mar 0.000000 227.919910 146.734356 73.249154 16.511949 9.132766 2011 Mar 0.000000 98.516816 141.485546 114.083357 25.596359 14.618269 2012 Mar 0.016492 159.148157 127.753656 47.260733 33.266823 15.013782 2013 Mar 0.000308 88.807005 102.169909 104.152958 6.107719 4.986616 2014 Mar 0.001233 140.983074 85.124268 18.017532 29.973154 19.959948 2015 Mar 0.001387 156.261738 85.625611 44.736618 24.295224 11.406969 2016 Mar 0.000000 86.784919 87.951962 32.514587 17.540399 8.584441 2017 Mar 0.000154 94.194538 140.553171 79.526230 45.869832 10.694595 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Mar 0.003567 145.483777 15.521933 320.159545 3.064967 0.000000 2002 Mar 1.120946 153.289352 17.100661 591.558397 2.814679 0.000000 2003 Mar 31.788821 174.533097 32.840379 586.880249 4.503062 0.318556 2004 Mar 0.242977 217.509412 16.932108 441.422056 2.536817 0.000000 2005 Mar 11.667674 178.737084 43.949049 510.079081 1.921703 0.004013 2006 Mar 0.395582 167.592705 26.106586 453.363643 2.328951 0.000000 2007 Mar 2.539570 207.076554 41.380696 526.736695 8.301925 0.005518 2008 Mar 7.724938 167.692920 22.906575 744.639888 4.976064 0.011538 2009 Mar 4.075124 145.991529 20.865577 503.819454 4.017334 0.000000 2010 Mar 0.209286 168.825340 11.843797 271.938638 2.131690 0.000000 2011 Mar 20.190513 153.826054 26.959367 573.366653 6.250836 0.015050 2012 Mar 2.638267 149.672731 19.966069 683.984957 7.778017 0.000502 2013 Mar 0.057871 143.947159 9.579174 396.828134 2.477427 0.000000 2014 Mar 41.697782 135.308692 90.566583 447.871081 2.015030 0.000000 2015 Mar 12.897623 146.950802 51.473874 477.701375 6.706869 0.028093 2016 Mar 1.627513 102.840953 29.753014 152.816333 1.628993 0.000502 2017 Mar 3.043362 111.447128 58.855179 548.839522 2.218654 0.049163 BIOME_NUM 12.0 13.0 14.0 2001 Mar 13.559701 9.062745 85.992960 2002 Mar 15.317272 14.328885 77.064729 2003 Mar 28.200050 8.927324 230.182079 2004 Mar 17.250903 5.462641 185.432485 2005 Mar 21.394398 9.696048 160.310540 2006 Mar 18.413510 8.031216 242.327365 2007 Mar 7.248840 25.590911 108.114730 2008 Mar 8.074503 9.986981 99.981726 2009 Mar 25.000604 8.004694 140.393718 2010 Mar 24.481529 5.185772 152.864324 2011 Mar 31.800186 7.309106 111.910131 2012 Mar 22.074356 9.286571 153.153497 2013 Mar 7.136525 9.044662 171.660599 2014 Mar 24.262971 9.961263 199.493545 2015 Mar 16.133829 15.651347 136.887490 2016 Mar 11.213238 6.606284 151.816070 2017 Mar 56.041908 7.587582 131.031727 Abr BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Abr 0.000000 23.675130 52.096810 137.597482 21.573224 6.919239 2002 Abr 0.000000 32.785237 50.562214 98.440487 14.291024 10.946285 2003 Abr 0.002620 68.361945 64.064831 141.184382 39.548257 7.528240 2004 Abr 0.000000 42.651253 70.943004 103.173202 39.675602 2.991071 2005 Abr 0.000617 59.959354 96.459497 232.783189 31.331040 4.752904 2006 Abr 0.000000 43.317114 51.671892 156.246042 68.094315 13.328355 2007 Abr 0.000308 75.180436 74.580793 82.199799 16.161058 16.290212 2008 Abr 0.000617 44.047907 91.161776 204.818651 103.763392 89.066942 2009 Abr 0.001233 82.919617 91.901561 281.986826 122.585547 12.108106 2010 Abr 0.000000 59.825814 74.849806 78.529869 38.862393 17.119442 2011 Abr 0.000000 36.284836 116.941168 521.844179 53.620579 22.697081 2012 Abr 0.000462 37.726821 94.356305 172.719214 47.346066 17.256523 2013 Abr 0.000154 83.766798 89.376508 170.859340 40.559404 63.565310 2014 Abr 0.000000 83.349640 89.116666 85.753487 72.750023 9.141755 2015 Abr 0.000000 62.071487 77.032480 66.739594 32.100647 12.296874 2016 Abr 0.000000 93.673856 107.751929 267.655831 24.626737 10.206945 2017 Abr 0.000308 60.064715 157.430679 259.585305 56.259529 0.991031 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Abr 2.560974 132.973701 42.203437 289.126569 0.702079 0.000000 2002 Abr 3.412783 144.868016 23.242847 426.961466 2.555907 0.000000 2003 Abr 12.111613 118.742724 74.783476 536.263449 3.637660 0.024080 2004 Abr 7.481961 107.863913 60.563242 291.702392 1.187807 0.022073 2005 Abr 5.500090 190.885832 67.004152 349.487932 1.187807 0.000502 2006 Abr 20.666955 106.652991 42.204271 387.235091 0.863281 0.000000 2007 Abr 7.538246 113.796038 34.375717 388.608231 2.163506 0.000000 2008 Abr 23.620736 131.826805 182.177863 547.002355 4.766077 0.016555 2009 Abr 15.439175 186.535978 76.559127 592.798958 1.756258 0.004013 2010 Abr 12.617386 104.058556 65.004040 171.860371 1.989577 0.005017 2011 Abr 19.048955 91.315203 106.399756 409.356858 1.639598 0.054180 2012 Abr 6.084742 172.914638 132.970281 305.414857 4.978185 0.005518 2013 Abr 8.367461 145.995426 16.242040 462.985097 1.580208 0.095316 2014 Abr 8.024201 88.906722 95.020732 628.699480 4.923037 0.011538 2015 Abr 4.339506 177.553443 108.767013 421.563603 5.130903 0.008027 2016 Abr 3.311311 130.202221 56.154986 243.812933 1.022363 0.006522 2017 Abr 9.359982 97.633710 67.168533 379.753842 1.677778 0.003010 BIOME_NUM 12.0 13.0 14.0 2001 Abr 20.769079 42.457831 64.666416 2002 Abr 18.349764 46.175674 43.448313 2003 Abr 9.224968 12.533053 172.347386 2004 Abr 60.792509 8.747299 134.646395 2005 Abr 25.131132 12.570022 87.511120 2006 Abr 23.649795 8.800744 79.125090 2007 Abr 9.255323 5.773667 87.547267 2008 Abr 23.668008 11.710482 77.968396 2009 Abr 38.812254 10.966672 198.842905 2010 Abr 32.486215 6.714379 74.426021 2011 Abr 24.876148 30.975997 82.992785 2012 Abr 27.814538 32.742897 87.294240 2013 Abr 20.857109 10.393244 117.187547 2014 Abr 51.069714 9.424804 149.683415 2015 Abr 30.343133 27.108267 232.423173 2016 Abr 9.100511 16.272997 144.478293 2017 Abr 46.762301 11.181658 108.620783 Mai BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Mai 0.000000 9.948100 58.293280 194.406676 1.519144 2.179819 2002 Mai 0.000154 21.687960 48.083014 259.369427 17.277404 18.759924 2003 Mai 0.000617 53.719126 100.271534 371.842000 42.043943 91.251255 2004 Mai 0.000771 25.977772 47.422710 90.004628 42.529100 4.678745 2005 Mai 0.000308 32.214936 89.235887 434.197605 27.980618 3.242761 2006 Mai 0.000771 18.705368 52.842710 259.469063 63.459370 6.301249 2007 Mai 0.000154 27.569223 63.820274 304.786891 41.387840 6.020345 2008 Mai 0.000617 22.302366 78.536507 332.236642 29.508067 17.622823 2009 Mai 0.000308 20.860381 63.083545 189.673961 52.255080 11.227190 2010 Mai 0.000000 23.389673 54.517927 117.969166 5.717378 2.804550 2011 Mai 0.000000 24.325677 53.622236 578.952282 23.468174 22.355501 2012 Mai 0.000308 17.802443 56.975728 396.020365 31.780900 72.657626 2013 Mai 0.000154 28.638154 80.835345 255.749314 33.235679 4.739420 2014 Mai 0.002466 21.675709 37.276640 77.998476 22.227943 6.566423 2015 Mai 0.000000 23.839911 38.441344 85.022822 4.246681 4.089969 2016 Mai 0.000000 34.980680 67.895209 157.275616 20.716413 20.085793 2017 Mai 0.000000 16.789256 54.548497 202.360960 6.187309 2.905676 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Mai 1.100731 360.944364 11.273052 95.712628 6.766259 0.000000 2002 Mai 7.086776 436.149574 22.342505 332.981836 26.929288 0.170064 2003 Mai 65.871461 489.978533 116.057365 307.128915 22.721057 1.783413 2004 Mai 24.138401 404.860466 75.526112 175.809334 20.389985 0.070735 2005 Mai 3.569747 412.360944 18.318084 403.068821 20.928740 0.254845 2006 Mai 6.422056 257.083453 96.340789 271.645070 28.734331 0.021572 2007 Mai 6.053032 373.176624 42.794208 284.902979 25.073339 0.055685 2008 Mai 20.122733 338.210767 56.065703 134.009043 4.121267 0.165047 2009 Mai 6.109713 232.606685 34.672771 359.885920 5.489367 0.137957 2010 Mai 2.485267 265.965217 18.344785 101.707096 5.934794 0.107356 2011 Mai 6.181853 313.276354 25.096935 229.522802 12.015944 0.134446 2012 Mai 7.674599 409.334474 38.753933 204.408536 22.899228 0.078259 2013 Mai 1.395237 340.854752 29.480992 400.909676 19.499129 0.312536 2014 Mai 7.249686 273.713447 30.131008 122.389433 5.296348 0.119898 2015 Mai 8.132808 304.752020 24.959255 171.244825 15.628150 0.246317 2016 Mai 15.270716 270.625179 35.902710 334.165578 14.576092 0.365211 2017 Mai 2.308484 323.876235 23.780216 126.234227 10.959643 0.007023 BIOME_NUM 12.0 13.0 14.0 2001 Mai 2.653051 42.684068 25.989465 2002 Mai 5.497340 34.714736 44.677301 2003 Mai 6.914932 14.200697 126.585685 2004 Mai 32.977970 23.333363 91.161935 2005 Mai 10.499890 22.835078 45.364088 2006 Mai 13.590056 17.035292 150.984696 2007 Mai 5.375919 10.286756 120.838362 2008 Mai 4.389373 14.379115 80.751690 2009 Mai 23.400881 12.101876 76.124915 2010 Mai 13.483813 11.582294 75.835742 2011 Mai 5.855532 33.761566 135.911529 2012 Mai 28.670557 20.209844 30.688534 2013 Mai 13.386676 19.002710 72.944007 2014 Mai 8.195924 8.763372 72.329513 2015 Mai 13.444351 17.339888 53.894705 2016 Mai 5.454843 17.217326 86.065253 2017 Mai 29.593357 31.725031 39.508325 Jun BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Jun 0.012331 46.332785 43.506737 3.371022 7.151395 3.089949 2002 Jun 0.005086 18.960196 23.932985 53.238900 17.788168 41.270484 2003 Jun 0.001541 29.766503 26.632286 85.238701 27.395108 13.573304 2004 Jun 0.003699 30.088101 29.817644 15.842143 23.683414 6.460803 2005 Jun 0.002004 24.963360 29.435523 181.204890 47.078918 13.600271 2006 Jun 0.003083 17.581919 27.378185 32.647435 84.112802 7.723749 2007 Jun 0.004778 32.542660 38.098964 65.676809 48.413965 3.961877 2008 Jun 0.002774 25.039318 32.599483 57.506647 4.985836 10.851901 2009 Jun 0.001387 30.837884 37.481457 35.719549 22.629357 5.991131 2010 Jun 0.000617 21.445996 27.992635 50.615149 78.823829 4.804590 2011 Jun 0.000462 20.926538 28.069060 197.130064 44.138908 30.991628 2012 Jun 0.011560 25.611456 26.543634 122.502610 97.373992 24.521836 2013 Jun 0.000771 32.344801 28.136313 70.774857 29.009760 13.393525 2014 Jun 0.002774 18.479943 28.582630 105.863378 25.660723 7.397900 2015 Jun 0.001695 25.143455 25.177170 7.921072 16.087696 8.759725 2016 Jun 0.002158 22.538817 27.796990 60.528943 3.630027 13.440717 2017 Jun 0.026049 16.990791 24.217283 191.616865 12.124773 13.229477 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Jun 4.647092 726.834858 40.303456 405.066977 49.474295 2002 Jun 19.215829 479.351371 30.773513 481.091611 22.337141 2003 Jun 39.553794 516.877145 24.638837 435.361298 22.731662 2004 Jun 9.640219 686.265912 114.286720 512.598083 21.582034 2005 Jun 15.339289 666.317713 47.410235 678.767023 34.444291 2006 Jun 17.903037 565.690938 96.106316 459.708499 25.525130 2007 Jun 11.965747 562.908323 68.961708 690.481332 18.854320 2008 Jun 21.725671 560.376243 61.024678 632.212826 28.662214 2009 Jun 7.481168 553.797736 22.770564 732.594131 29.531859 2010 Jun 17.815042 571.040707 99.994726 574.616684 34.297936 2011 Jun 21.578616 592.646895 57.168810 530.325801 34.722153 2012 Jun 40.369928 737.452555 33.136599 831.261378 24.464588 2013 Jun 24.323508 633.827704 13.668680 761.202803 14.569729 2014 Jun 8.870857 589.431131 31.023840 488.127773 24.297023 2015 Jun 35.863153 515.917315 41.262208 545.790204 20.739964 2016 Jun 13.304699 585.744361 13.601092 758.920549 16.287808 2017 Jun 10.505900 572.259980 34.535091 345.463210 20.595730 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Jun 0.206685 14.910511 37.437618 2.385681 2002 Jun 0.892459 17.296436 15.433147 6.940163 2003 Jun 4.474334 22.478081 7.319955 21.037370 2004 Jun 17.104708 24.208332 101.585679 36.472003 2005 Jun 2.939746 20.219648 30.920141 24.832771 2006 Jun 0.163041 14.391436 70.654286 30.363214 2007 Jun 1.069546 19.096503 22.022955 57.328640 2008 Jun 0.657179 23.625510 15.253926 20.531316 2009 Jun 0.677747 22.365766 14.906332 27.037719 2010 Jun 1.528066 19.655040 41.327449 37.881724 2011 Jun 0.349158 13.077052 23.037607 33.218802 2012 Jun 1.951971 21.169769 27.853282 17.314261 2013 Jun 0.722897 16.091331 8.076222 19.374622 2014 Jun 0.199160 18.316373 12.800278 22.447090 2015 Jun 2.782224 34.462343 18.921538 21.615717 2016 Jun 2.212335 24.126372 3.559517 25.953319 2017 Jun 1.250646 38.171758 36.515390 17.928755 Jul BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Jul 0.025586 27.403217 23.248225 0.016606 25.104973 4.184353 2002 Jul 0.011868 40.711863 40.098220 13.185180 24.835749 36.196226 2003 Jul 0.008323 56.345204 45.426511 4.965200 6.426773 16.953146 2004 Jul 0.013872 46.576587 43.531192 0.730665 10.345403 11.406969 2005 Jul 0.007090 69.533787 40.932771 6.111015 14.345007 10.678864 2006 Jul 0.008786 35.078691 49.522845 0.016606 14.244653 19.993656 2007 Jul 0.061962 54.681470 45.594644 1.046179 20.581455 30.643307 2008 Jul 0.005703 40.465611 53.279856 0.215878 22.499244 23.285857 2009 Jul 0.004932 53.434895 28.897497 2.324843 12.885383 3.687714 2010 Jul 0.002774 48.006320 49.672637 0.232484 26.135499 6.069784 2011 Jul 0.002929 47.389464 30.887582 4.732716 5.042588 4.979874 2012 Jul 0.007553 51.647422 47.019190 0.481575 9.053958 11.597984 2013 Jul 0.004624 46.532482 29.151225 0.232484 4.751909 9.779970 2014 Jul 0.001850 37.260657 27.289533 0.265696 6.905702 32.270305 2015 Jul 0.009865 49.177549 28.506206 3.121932 11.771806 29.641040 2016 Jul 0.006628 50.751236 51.726918 1.295270 8.793731 6.872047 2017 Jul 0.015876 44.176546 27.524920 0.813695 6.546505 57.333726 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Jul 32.171323 530.560284 161.060291 787.093604 26.263268 2002 Jul 57.357341 814.174340 111.809319 883.222909 69.032815 2003 Jul 34.327202 846.399895 31.670518 741.884137 24.683060 2004 Jul 28.758144 818.818713 60.663373 781.222245 29.005830 2005 Jul 26.066366 962.167370 141.452189 676.816217 32.274421 2006 Jul 36.565528 776.525525 112.990862 807.861170 32.369870 2007 Jul 18.372345 971.532946 82.919932 751.742339 57.710466 2008 Jul 39.238676 781.773410 124.001905 780.474120 31.118430 2009 Jul 22.155341 797.223660 100.269251 574.872372 22.568339 2010 Jul 26.419139 812.357679 86.846726 810.863139 33.508892 2011 Jul 34.131393 932.011239 32.047677 873.809794 25.728755 2012 Jul 85.249011 837.244212 82.343346 663.009053 20.739964 2013 Jul 64.322827 765.446286 13.370791 555.459007 25.370291 2014 Jul 68.978639 789.900227 78.223801 810.086605 30.893595 2015 Jul 51.616652 877.116669 55.152845 834.954653 26.581431 2016 Jul 39.127295 784.563820 49.426201 614.863906 18.928558 2017 Jul 25.785733 718.447484 101.440781 719.885479 26.687485 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Jul 3.521676 20.975495 42.202259 2.349534 2002 Jul 1.341447 33.026539 72.953225 16.193714 2003 Jul 16.025631 35.318362 13.975666 26.097905 2004 Jul 11.108830 35.139266 22.263659 22.627824 2005 Jul 3.118338 30.759000 100.478604 28.339000 2006 Jul 2.398953 20.250004 36.399258 17.061235 2007 Jul 1.543618 50.526355 39.236263 21.868743 2008 Jul 0.894967 21.312438 14.471941 15.868394 2009 Jul 3.174525 38.542092 23.747260 22.591677 2010 Jul 18.182281 19.472909 46.229521 14.386380 2011 Jul 1.311849 11.377157 40.657578 22.121770 2012 Jul 1.666525 36.119741 30.464854 16.880501 2013 Jul 1.603817 14.628207 8.517043 39.291445 2014 Jul 0.648149 13.708442 19.519881 33.038069 2015 Jul 3.372682 29.053033 22.235128 19.699942 2016 Jul 7.814909 19.582188 9.605633 14.422527 2017 Jul 5.416959 48.623079 102.125755 27.543773 Ago BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Ago 0.021425 82.596794 94.099520 0.000000 50.715174 17.067755 2002 Ago 0.008632 116.581438 106.217332 1.129209 54.015764 34.587205 2003 Ago 0.005857 99.114070 83.250348 0.116242 10.890772 41.454758 2004 Ago 0.000617 101.266633 117.521991 0.298908 37.110014 8.004654 2005 Ago 0.011868 154.063845 128.780796 0.398544 33.324959 12.249682 2006 Ago 0.033293 98.499664 122.617953 0.381938 24.237089 24.634198 2007 Ago 0.025586 123.725203 125.656577 0.000000 33.171314 54.048267 2008 Ago 0.008323 83.138304 58.797679 2.922660 74.969565 10.694595 2009 Ago 0.004316 62.814532 42.855603 1.627390 39.596703 20.627377 2010 Ago 0.012177 157.809696 248.363183 0.000000 36.853940 23.919577 2011 Ago 0.009402 71.233663 66.758018 0.083030 18.042858 11.042916 2012 Ago 0.031752 83.709217 113.544879 0.000000 9.152235 47.502069 2013 Ago 0.005241 69.721233 53.539698 2.773205 17.278788 36.384994 2014 Ago 0.006782 67.180302 53.408249 0.963149 11.391155 36.032178 2015 Ago 0.003545 75.837109 81.257207 0.714059 17.249720 81.147683 2016 Ago 0.010173 78.819089 110.866977 3.653324 19.042240 16.083466 2017 Ago 0.015568 64.544423 54.050211 0.631029 11.328866 88.520863 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Ago 17.046473 1240.009087 187.251154 1231.583918 54.378242 2002 Ago 46.529981 1027.067217 222.770864 974.996044 52.233826 2003 Ago 28.845742 1136.837427 49.532172 723.076847 63.884943 2004 Ago 15.795912 1029.077625 82.954978 888.279854 39.469140 2005 Ago 20.119166 1069.821389 86.905970 1124.602069 44.443083 2006 Ago 5.837008 1111.337917 154.950647 1027.279552 50.908148 2007 Ago 5.538934 1165.708589 129.941327 900.979036 33.860992 2008 Ago 4.886106 904.596523 218.920003 667.081124 53.924330 2009 Ago 11.313711 994.818835 52.012911 685.253929 51.317518 2010 Ago 21.872726 1256.384091 117.253928 1065.945295 55.977540 2011 Ago 8.148266 1067.495306 50.418329 519.511135 67.573509 2012 Ago 30.127617 1088.513013 66.778023 750.653296 44.638223 2013 Ago 40.857072 1027.416853 23.762693 415.701714 40.113949 2014 Ago 33.040175 977.326442 114.043069 537.646060 52.363212 2015 Ago 14.210811 1004.760086 138.258019 645.641193 31.228727 2016 Ago 5.492955 961.328354 62.429846 467.663244 28.235876 2017 Ago 40.606564 884.540316 113.463145 663.113222 36.071162 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Ago 3.269339 35.227296 119.378443 10.590978 2002 Ago 8.248848 23.753002 190.515342 51.545170 2003 Ago 13.101436 85.550271 24.673908 16.446741 2004 Ago 12.954449 24.323682 17.791156 33.074215 2005 Ago 5.107433 38.080692 19.368788 27.724506 2006 Ago 1.594286 16.674152 64.809895 29.531840 2007 Ago 4.182366 90.176415 48.029773 13.012806 2008 Ago 1.853144 27.347067 69.790728 16.374448 2009 Ago 2.638748 41.774929 16.531382 41.460246 2010 Ago 8.679777 21.773839 67.479332 7.410070 2011 Ago 1.060516 20.377496 153.561140 37.773284 2012 Ago 2.208823 57.608240 61.137460 34.917696 2013 Ago 2.466176 26.815850 10.589343 36.652737 2014 Ago 0.844299 27.189220 33.595606 32.025961 2015 Ago 4.995562 25.128096 44.712165 43.845927 2016 Ago 8.049688 32.039992 16.054797 31.989815 2017 Ago 0.864867 44.813492 75.250154 8.530617 Set BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Set 0.002774 60.699336 81.425340 0.531393 34.585260 10.092336 2002 Set 0.002158 134.690165 92.635233 0.033212 46.525244 56.965179 2003 Set 0.002774 112.037411 99.085431 0.531393 23.748471 12.501372 2004 Set 0.002158 141.682626 278.455954 0.049818 34.511206 2.800056 2005 Set 0.005549 163.125559 143.059883 0.000000 27.800674 6.793394 2006 Set 0.005549 136.191569 105.954433 0.116242 26.828976 16.283470 2007 Set 0.001541 211.957620 303.079813 0.016606 21.596063 16.231784 2008 Set 0.006628 101.886551 135.065918 0.431757 41.672290 5.775396 2009 Set 0.002620 100.949323 78.496767 0.614423 35.068340 8.676577 2010 Set 0.002158 185.279683 236.126149 0.033212 29.164789 7.382169 2011 Set 0.012793 112.613225 142.717503 0.763877 37.641541 16.429541 2012 Set 0.012639 102.774162 170.019264 0.033212 20.890128 22.721801 2013 Set 0.004778 77.650309 73.419147 1.693814 19.818076 5.258532 2014 Set 0.003083 85.466674 82.036733 0.016606 33.822573 14.780070 2015 Set 0.010481 117.677322 155.248006 0.016606 43.558242 19.568929 2016 Set 0.000771 83.842144 86.081099 0.415151 24.799760 8.445112 2017 Set 0.038688 135.997998 130.547723 0.033212 14.984500 44.758194 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Set 2.031418 1041.333825 59.118856 602.505641 41.957172 2002 Set 11.333530 846.622593 330.111849 554.654062 54.346426 2003 Set 13.546884 804.655102 101.769544 393.835635 32.446229 2004 Set 1.708770 1207.745116 65.342816 794.840010 37.352297 2005 Set 3.478581 930.333207 108.318094 658.491894 56.789915 2006 Set 6.130325 1066.804384 129.574181 488.118303 35.326662 2007 Set 5.014927 1253.876509 113.957958 709.506431 26.099944 2008 Set 1.744840 1183.549505 79.214261 633.197699 46.761428 2009 Set 4.239223 990.205640 31.184884 551.813082 38.663128 2010 Set 1.388103 1241.331358 96.044569 620.422757 44.856694 2011 Set 5.684800 1325.134391 54.897511 443.855829 39.252789 2012 Set 4.175803 1163.962634 32.892113 484.159870 36.450837 2013 Set 2.033796 876.446347 14.618253 345.709428 35.093342 2014 Set 3.059217 945.812965 47.732322 265.906290 30.325145 2015 Set 3.762781 932.665415 63.716526 400.275190 23.022251 2016 Set 19.959427 778.828782 33.844189 541.642372 18.355865 2017 Set 5.097770 1082.794121 100.344350 588.764766 25.892078 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Set 0.028093 30.625437 147.242173 5.385856 2002 Set 0.579421 21.309403 127.365858 57.979280 2003 Set 3.122352 22.217026 38.661227 30.182481 2004 Set 0.398822 32.137129 34.109964 22.736264 2005 Set 0.349158 13.990746 22.521240 16.519034 2006 Set 0.253340 25.428614 77.414878 50.027010 2007 Set 2.121534 31.882145 144.079274 8.892084 2008 Set 0.850319 27.198326 78.636879 11.856112 2009 Set 0.140967 38.876000 16.841202 55.882772 2010 Set 0.054180 17.964252 54.040929 12.470606 2011 Set 0.025585 28.612882 392.616192 36.038243 2012 Set 0.416380 32.571210 134.570485 29.170373 2013 Set 0.272403 20.058766 22.884907 12.542899 2014 Set 0.220230 25.061315 36.647194 39.616765 2015 Set 0.428420 37.601079 25.200723 126.260365 2016 Set 0.367719 30.786320 9.806956 11.386205 2017 Set 0.137456 18.040140 134.035231 8.133004 Out BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Out 0.001387 33.984645 63.303647 0.249090 27.503074 2.988824 2002 Out 0.000925 92.179191 123.950790 0.182666 18.431815 3.498946 2003 Out 0.002466 87.248021 94.628375 0.000000 15.073780 3.750636 2004 Out 0.001695 76.937281 63.581831 0.116242 24.932642 2.422520 2005 Out 0.002466 70.848358 119.429538 0.464969 46.940500 2.222516 2006 Out 0.002620 97.066869 63.749964 0.149454 14.935362 2.831517 2007 Out 0.001079 93.341844 134.423955 0.016606 16.076623 14.937376 2008 Out 0.000000 65.881658 76.124561 0.132848 19.790393 3.303437 2009 Out 0.000617 70.221701 53.032242 0.016606 16.844846 2.564096 2010 Out 0.002929 87.004219 124.308455 0.298908 26.783298 2.777583 2011 Out 0.000617 54.040112 41.571677 0.282302 39.491505 11.624951 2012 Out 0.003237 76.394546 104.805014 0.016606 15.914673 3.986596 2013 Out 0.001387 42.950186 62.710595 0.016606 19.587609 1.950601 2014 Out 0.003083 86.824124 71.872322 0.747271 45.635213 3.018038 2015 Out 0.001079 98.924786 122.880852 0.066424 37.222133 3.355123 2016 Out 0.000308 61.889555 118.069188 0.315514 18.074003 3.098938 2017 Out 0.012485 63.255581 88.040614 0.365332 17.111302 3.964124 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Out 0.386861 815.557296 13.036188 485.267852 7.249867 0.011538 2002 Out 0.925138 741.187762 74.974558 524.568080 16.578396 0.000000 2003 Out 0.831593 760.323669 173.502369 314.638573 30.486347 0.142974 2004 Out 0.181936 1104.017267 58.715830 420.152583 17.032308 0.006522 2005 Out 3.848794 675.623718 129.482394 690.765430 19.991221 0.001505 2006 Out 0.407076 630.683761 29.124694 266.332437 12.764686 0.003512 2007 Out 2.075416 787.905408 54.210781 331.447707 7.572272 0.002508 2008 Out 1.806674 738.215297 43.808032 511.007134 15.184844 0.001003 2009 Out 0.068573 631.543933 35.360335 380.965994 12.334106 0.000000 2010 Out 0.286182 637.623040 35.907717 411.459183 28.927350 0.000000 2011 Out 2.225245 733.761331 31.554533 344.061659 19.815171 0.001003 2012 Out 0.227519 672.721959 24.015523 230.536085 7.205324 0.001003 2013 Out 0.265174 503.359359 23.729316 288.700422 21.229934 0.001003 2014 Out 0.998070 738.201935 46.927104 302.583346 23.230117 0.000000 2015 Out 0.590598 558.773372 42.068260 398.447493 20.385743 0.135951 2016 Out 0.199773 642.144371 7.717577 592.751609 7.661357 0.007023 2017 Out 0.120894 723.049544 23.725978 368.456211 28.800085 0.000000 BIOME_NUM 12.0 13.0 14.0 2001 Out 16.470772 85.292188 7.373923 2002 Out 30.959345 244.736296 73.305474 2003 Out 50.107452 48.849129 11.133178 2004 Out 15.651180 127.542267 58.666067 2005 Out 22.414335 35.092467 16.374448 2006 Out 13.693264 95.253451 104.030154 2007 Out 40.296627 74.290154 9.578871 2008 Out 14.418755 50.108904 13.952620 2009 Out 18.689743 34.821626 16.663621 2010 Out 21.066561 98.507569 15.217754 2011 Out 20.386603 376.927072 9.795751 2012 Out 10.688092 327.186255 18.145635 2013 Out 26.557830 47.790275 18.687835 2014 Out 22.948588 115.005999 39.580618 2015 Out 28.330578 99.578076 85.089292 2016 Out 20.829790 20.329995 16.410594 2017 Out 66.311098 105.064426 7.554657 Nov BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Nov 0.001695 32.765022 30.459607 0.730665 5.721531 0.242701 2002 Nov 0.000000 42.109743 54.890877 0.780483 12.106087 3.285459 2003 Nov 0.000000 45.118676 62.031949 0.116242 4.203079 1.973073 2004 Nov 0.000000 56.242293 83.302317 0.963149 4.391329 2.510162 2005 Nov 0.000000 73.544268 83.990134 0.415151 5.777590 2.312406 2006 Nov 0.000308 46.163103 38.636990 0.348726 10.610474 0.573045 2007 Nov 0.000771 40.735141 77.689728 0.083030 12.010578 1.471939 2008 Nov 0.000000 40.598538 55.401390 1.643996 8.755666 1.759586 2009 Nov 0.000000 56.155921 48.694407 1.295270 6.261363 1.793294 2010 Nov 0.001233 39.582288 39.911745 2.275025 15.320165 1.426995 2011 Nov 0.001079 40.401904 43.405857 0.265696 12.699210 1.561829 2012 Nov 0.001695 36.463094 43.989737 0.199272 5.424623 1.197777 2013 Nov 0.000000 32.659048 41.565563 0.863513 7.188768 2.555107 2014 Nov 0.000000 33.488465 46.829658 0.498181 22.663270 0.878669 2015 Nov 0.001541 68.564705 84.552616 0.464969 13.839779 0.793274 2016 Nov 0.000154 36.569680 65.409896 1.378300 7.731368 0.961817 2017 Nov 0.000154 34.774244 41.522766 0.298908 8.023431 3.116916 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Nov 0.000396 800.815227 2.544155 123.222787 0.434822 0.0 2002 Nov 0.116930 684.890193 18.111982 972.666440 6.429007 0.0 2003 Nov 0.000000 769.524448 14.947016 299.770776 4.019455 0.0 2004 Nov 0.088391 981.341135 3.433650 617.970044 2.791347 0.0 2005 Nov 0.099490 1107.032602 8.068869 403.646487 5.953884 0.0 2006 Nov 0.002775 1009.874352 3.816650 386.373327 1.387189 0.0 2007 Nov 0.091959 805.529750 4.673602 350.273936 2.861343 0.0 2008 Nov 0.000000 888.503232 9.998889 347.859103 2.131690 0.0 2009 Nov 0.156568 825.063173 6.930716 1548.362697 1.295983 0.0 2010 Nov 0.000000 618.229916 5.807583 211.757205 6.422644 0.0 2011 Nov 0.004756 671.572836 7.554864 484.699656 5.035455 0.0 2012 Nov 0.000000 734.521846 6.865631 303.776558 3.033151 0.0 2013 Nov 0.000000 583.802989 7.564043 344.904484 5.300590 0.0 2014 Nov 0.000000 598.366900 12.508832 283.349909 1.667172 0.0 2015 Nov 0.000000 727.556957 9.015938 1035.272177 8.745231 0.0 2016 Nov 0.177972 744.282711 5.754180 735.075254 3.260107 0.0 2017 Nov 0.002775 641.145570 6.818069 280.262711 8.420705 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Nov 5.075402 103.090578 2.927881 2002 Nov 63.445560 226.732565 11.458499 2003 Nov 13.693264 23.873438 9.181258 2004 Nov 10.023312 86.982738 20.314436 2005 Nov 26.433373 26.898907 7.988417 2006 Nov 28.974110 195.605475 23.748371 2007 Nov 21.461179 47.199969 13.121246 2008 Nov 7.151703 35.859584 11.711525 2009 Nov 21.324580 44.666757 9.759604 2010 Nov 9.983850 40.265381 4.735216 2011 Nov 12.849388 77.105057 5.855763 2012 Nov 11.662497 151.177011 11.892259 2013 Nov 12.217998 52.933452 5.205122 2014 Nov 6.201583 93.218121 9.181258 2015 Nov 57.073988 91.097199 14.747847 2016 Nov 25.817161 46.270509 13.771886 2017 Nov 36.271518 130.560101 7.988417 Dez BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Dez 0.000000 27.441196 17.546983 0.232484 12.718589 0.168543 2002 Dez 0.001233 49.350906 36.506285 0.448363 5.864102 0.294388 2003 Dez 0.000000 63.737671 72.798582 1.594178 2.405022 0.525853 2004 Dez 0.000000 56.472618 202.523981 3.487264 1.293521 0.660687 2005 Dez 0.001079 28.440907 25.216911 1.976116 2.447932 1.379803 2006 Dez 0.000000 59.290430 41.220126 0.464969 22.976788 0.869680 2007 Dez 0.000925 46.620079 36.151677 1.660602 1.305979 0.361805 2008 Dez 0.000771 38.321012 33.427921 0.315514 1.549596 0.083148 2009 Dez 0.000000 50.198700 36.604108 0.315514 3.565662 0.188768 2010 Dez 0.000000 54.009483 29.047289 1.843268 3.798205 0.404502 2011 Dez 0.000000 63.150832 31.795501 3.570294 2.027140 0.979795 2012 Dez 0.000925 43.851885 85.674522 1.942904 3.400252 0.301130 2013 Dez 0.000000 37.214714 26.277678 0.581211 3.786440 1.977567 2014 Dez 0.000000 33.865806 40.862461 1.594178 1.921250 0.328096 2015 Dez 0.000000 63.341340 144.420233 1.345088 1.745458 1.155079 2016 Dez 0.000000 32.838530 21.404874 3.038902 3.268754 1.341600 2017 Dez 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Dez 0.000000 1148.667716 18.591775 493.478286 3.586754 0.0 2002 Dez 0.000000 1334.297312 6.571080 2094.616927 4.397008 0.0 2003 Dez 0.001585 1379.184378 13.287349 1189.565819 7.075938 0.0 2004 Dez 0.000000 1051.410922 9.673464 1363.433816 5.069392 0.0 2005 Dez 0.001585 1240.612042 5.959448 1131.022684 5.147872 0.0 2006 Dez 0.012288 1278.806468 2.261286 1009.419256 17.602880 0.0 2007 Dez 0.000000 1161.896551 2.668484 1172.624106 4.078845 0.0 2008 Dez 0.000000 1055.165616 2.281312 1175.985933 4.920916 0.0 2009 Dez 0.000000 1115.934409 0.766000 1142.916922 1.158112 0.0 2010 Dez 0.000000 1062.367121 3.326844 744.942926 2.801953 0.0 2011 Dez 0.000000 1131.710912 7.888633 1153.684237 7.235019 0.0 2012 Dez 0.004756 1018.605795 2.444024 1107.186859 3.991881 0.0 2013 Dez 0.000000 892.228974 5.115846 914.284294 2.473184 0.0 2014 Dez 0.000000 923.814272 6.963258 778.135046 3.567664 0.0 2015 Dez 0.000000 806.393262 3.884238 1914.820751 5.898736 0.0 2016 Dez 0.000000 1010.840306 42.863465 1370.735136 4.015213 0.0 2017 Dez 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Dez 4.198134 95.979580 3.036321 2002 Dez 72.807126 60.272696 15.868394 2003 Dez 12.639936 11.206974 28.953493 2004 Dez 71.608093 42.905483 24.688185 2005 Dez 25.841445 7.581956 10.916298 2006 Dez 37.106288 35.691614 30.941561 2007 Dez 16.874497 44.220310 9.578871 2008 Dez 9.804754 4.761829 14.603260 2009 Dez 16.807716 10.875856 11.675379 2010 Dez 3.241943 6.160239 12.398312 2011 Dez 5.852497 48.275299 16.627474 2012 Dez 6.389785 20.822251 18.615542 2013 Dez 9.404064 11.012884 27.001572 2014 Dez 3.584958 17.314974 15.434634 2015 Dez 7.066708 18.853225 32.387428 2016 Dez 41.022118 12.427770 22.916997 2017 Dez 0.000000 0.000000 0.000000
intra_biome
| BIOME_NUM | 0.0 | 1.0 | 2.0 | 3.0 | 4.0 | 5.0 | 6.0 | 7.0 | 8.0 | 9.0 | 10.0 | 11.0 | 12.0 | 13.0 | 14.0 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jan | 0.023428 | 806.170362 | 4464.016780 | 94.720738 | 98.663361 | 13.905895 | 0.156568 | 13382.845022 | 337.849452 | 20120.968649 | 199.659802 | 0.000000 | 446.268071 | 421.769032 | 611.059681 |
| Fev | 0.017571 | 1351.098356 | 3039.033593 | 232.351432 | 113.069964 | 33.090546 | 0.274291 | 5941.738253 | 172.039626 | 12349.343812 | 119.518867 | 0.000000 | 347.631642 | 175.038796 | 1724.630552 |
| Mar | 0.024970 | 2169.837995 | 1918.588391 | 1156.991232 | 463.513904 | 237.714845 | 141.921417 | 2670.725290 | 536.600621 | 8232.005701 | 65.673017 | 0.432935 | 347.604322 | 169.724032 | 2538.617713 |
| Abr | 0.006320 | 989.661960 | 1450.297921 | 3062.116878 | 823.148837 | 317.206315 | 169.486078 | 2242.725916 | 1250.841515 | 6832.633484 | 41.762032 | 0.256350 | 472.962500 | 304.549687 | 1943.209546 |
| Mai | 0.006628 | 424.426734 | 1045.702389 | 4307.335894 | 465.541044 | 297.489068 | 191.173300 | 5807.769091 | 699.840424 | 4055.726718 | 267.962964 | 4.030362 | 223.384467 | 351.173013 | 1329.655744 |
| Jun | 0.082770 | 439.594524 | 505.398977 | 1337.399034 | 590.088674 | 223.062867 | 320.103550 | 10116.740886 | 830.667074 | 9863.590182 | 443.117877 | 39.181902 | 363.662262 | 487.625324 | 422.663167 |
| Jul | 0.200220 | 799.173001 | 662.309972 | 39.788024 | 230.270337 | 315.574821 | 690.642954 | 13816.263759 | 1425.689808 | 12668.120747 | 533.465470 | 83.145156 | 478.414307 | 645.083528 | 360.382432 |
| Ago | 0.214555 | 1590.655214 | 1661.586223 | 15.692689 | 498.371156 | 564.002239 | 350.269219 | 17947.039070 | 1871.647079 | 13289.007531 | 800.622421 | 82.119756 | 638.653731 | 1033.269412 | 472.907057 |
| Set | 0.117142 | 2064.521677 | 2393.454394 | 5.330532 | 517.016136 | 275.463911 | 94.390194 | 17672.101896 | 1462.682274 | 9077.699260 | 622.992203 | 9.766379 | 454.360788 | 1496.675312 | 535.079352 |
| Out | 0.038379 | 1258.992673 | 1526.483621 | 3.437446 | 420.348771 | 72.295821 | 15.445517 | 12094.693021 | 847.861190 | 6862.141799 | 296.449128 | 0.315546 | 439.820611 | 1986.376150 | 521.560493 |
| Nov | 0.008632 | 755.936134 | 942.285238 | 12.620575 | 162.728312 | 28.414049 | 0.742013 | 13192.053837 | 134.414667 | 8729.243551 | 69.189775 | 0.000000 | 369.660464 | 1473.536842 | 183.589004 |
| Dez | 0.004932 | 748.146110 | 881.479132 | 24.410849 | 74.074689 | 11.020444 | 0.020215 | 17611.936057 | 134.546506 | 18756.852998 | 83.021367 | 0.000000 | 344.250064 | 448.362941 | 295.643722 |
intra_biome[1]
Jan 806.170362 Fev 1351.098356 Mar 2169.837995 Abr 989.661960 Mai 424.426734 Jun 439.594524 Jul 799.173001 Ago 1590.655214 Set 2064.521677 Out 1258.992673 Nov 755.936134 Dez 748.146110 Name: 1.0, dtype: float64
intra_biome = [ b for a, b in biomes_pa.groupby(biomes_pa.index.get_level_values(level=1),sort=False)]
intra_biome
[BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Jan 0.005549 27.050378 294.159587 2.457691 0.708703 0.235960 2002 Jan 0.000000 38.795751 314.488409 2.457691 10.087252 0.020225 2003 Jan 0.002929 69.941757 263.764176 1.942904 27.791677 1.289913 2004 Jan 0.000000 46.164329 356.735676 5.131260 1.690783 0.038203 2005 Jan 0.000000 81.842110 281.295875 7.339861 1.352349 0.379783 2006 Jan 0.004316 33.638544 229.140982 9.664704 6.664161 2.114649 2007 Jan 0.001387 59.109110 429.797157 2.457691 8.703067 1.114629 2008 Jan 0.000308 51.483254 265.298773 13.816209 0.824975 0.159554 2009 Jan 0.007861 38.517033 158.662637 6.293682 2.587043 1.373061 2010 Jan 0.000000 38.863134 300.401911 2.424479 1.015992 1.116876 2011 Jan 0.000154 33.209746 333.621958 4.931988 0.574437 0.858444 2012 Jan 0.000000 37.056059 265.622811 2.025934 2.193934 0.707879 2013 Jan 0.000462 49.828096 235.612579 4.965200 6.802580 0.979795 2014 Jan 0.000000 33.885409 170.587860 1.262058 8.274661 0.955075 2015 Jan 0.000000 55.159273 272.210573 2.291631 2.052055 1.155079 2016 Jan 0.000000 83.399258 207.277563 3.786173 4.299972 0.444953 2017 Jan 0.000462 28.227121 85.338256 21.471584 13.039720 0.961817 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Jan 0.054303 816.822779 136.908757 1317.788732 8.662509 0.0 2002 Jan 0.000000 772.452931 17.127363 1716.037356 5.985700 0.0 2003 Jan 0.000000 998.604149 24.766503 735.198363 69.471880 0.0 2004 Jan 0.000000 667.036472 6.575252 1290.950938 9.954249 0.0 2005 Jan 0.000000 1054.433495 7.387145 1131.155263 9.205507 0.0 2006 Jan 0.094337 703.938134 9.546631 1314.815173 14.864560 0.0 2007 Jan 0.000000 877.533672 2.635942 1228.657709 12.928010 0.0 2008 Jan 0.000000 840.320232 3.999389 1122.035716 5.707838 0.0 2009 Jan 0.000000 622.330905 9.943817 1177.311724 7.705900 0.0 2010 Jan 0.000000 770.405220 2.907129 932.210880 7.616814 0.0 2011 Jan 0.000000 722.594126 4.052792 1213.089136 9.807895 0.0 2012 Jan 0.000000 820.254560 7.760967 1352.808550 8.598876 0.0 2013 Jan 0.000000 849.253217 8.885769 1189.944617 6.095997 0.0 2014 Jan 0.007927 694.905492 29.568607 1221.185930 4.950611 0.0 2015 Jan 0.000000 674.351554 13.415016 1140.691487 6.643237 0.0 2016 Jan 0.000000 992.973780 3.777432 1048.236517 5.807529 0.0 2017 Jan 0.000000 504.634307 48.590944 988.850558 5.652690 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Jan 87.917982 150.205357 6.904017 2002 Jan 21.373149 71.646836 30.182481 2003 Jan 37.652683 16.461863 54.364612 2004 Jan 4.295271 9.402703 19.989116 2005 Jan 42.151334 11.301005 100.668513 2006 Jan 20.310714 1.830793 15.109314 2007 Jan 8.769639 5.669590 39.580618 2008 Jan 21.600813 30.440342 46.340048 2009 Jan 21.464215 4.134955 34.773109 2010 Jan 23.294638 5.230377 25.555705 2011 Jan 5.706792 12.747235 29.170373 2012 Jan 16.276498 40.269399 40.195112 2013 Jan 7.804341 30.202451 16.808208 2014 Jan 58.476401 8.624335 14.711700 2015 Jan 11.122173 11.515187 69.112458 2016 Jan 30.476696 9.644612 38.532364 2017 Jan 27.574732 2.441994 29.061933 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Fev 0.000000 59.446635 130.801451 8.435858 0.742616 0.208993 2002 Fev 0.003391 42.650640 112.967112 3.819385 3.579504 2.546118 2003 Fev 0.000000 50.174197 229.266318 8.286404 8.082951 1.269688 2004 Fev 0.000000 113.045085 178.939490 8.136950 3.245915 0.921367 2005 Fev 0.001387 107.077450 143.643764 16.340324 2.214697 1.806778 2006 Fev 0.000000 52.210985 265.097013 21.886734 3.701312 1.543851 2007 Fev 0.002158 110.519468 119.723007 5.197684 5.190696 2.851742 2008 Fev 0.002312 74.455769 164.935530 6.559378 17.835231 3.591083 2009 Fev 0.000000 120.912905 306.726773 42.710683 12.481893 2.791067 2010 Fev 0.000000 94.073863 79.355774 7.638769 2.551746 2.905676 2011 Fev 0.000771 49.249832 149.883031 14.380813 4.991373 0.723610 2012 Fev 0.002004 104.831164 157.024103 18.067350 6.722297 1.182046 2013 Fev 0.002774 71.312684 160.668006 15.410387 4.876486 2.449487 2014 Fev 0.000154 68.994115 188.003394 4.201323 15.160292 2.487690 2015 Fev 0.000000 61.403788 179.355238 8.469070 10.694910 1.022492 2016 Fev 0.000000 94.105716 147.737041 17.868078 5.158859 1.487670 2017 Fev 0.002620 76.634060 324.906548 24.942242 5.839187 3.301189 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Fev 0.000000 317.193617 20.659474 510.126430 8.036789 0.0 2002 Fev 0.001189 357.576610 21.891082 1204.121108 5.519062 0.0 2003 Fev 0.008324 377.099455 21.467196 400.701337 18.175573 0.0 2004 Fev 0.000000 479.696553 4.898896 776.989184 7.016547 0.0 2005 Fev 0.046772 365.888823 11.448281 536.149810 5.930552 0.0 2006 Fev 0.000396 266.026460 7.617446 618.604530 7.642267 0.0 2007 Fev 0.000000 470.997401 7.161851 1031.948230 13.456160 0.0 2008 Fev 0.042808 379.819157 9.573333 987.373248 7.472581 0.0 2009 Fev 0.000000 257.423068 6.117154 777.737309 10.439978 0.0 2010 Fev 0.077689 308.803459 3.132423 380.615606 6.129934 0.0 2011 Fev 0.000000 394.403668 10.406921 1092.981957 5.190294 0.0 2012 Fev 0.001189 390.678482 4.648569 893.109520 5.597542 0.0 2013 Fev 0.000000 343.137410 5.544740 604.304929 5.018486 0.0 2014 Fev 0.005946 360.093100 6.239814 714.430797 2.948307 0.0 2015 Fev 0.017837 308.463287 3.942648 920.098834 6.204172 0.0 2016 Fev 0.000000 290.776033 9.225379 373.768844 2.536817 0.0 2017 Fev 0.072140 273.661670 18.064419 526.282139 2.203807 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Fev 11.783918 6.511449 62.714495 2002 Fev 15.751352 21.735638 72.835567 2003 Fev 46.558920 4.743344 66.039990 2004 Fev 8.642147 3.420480 124.163857 2005 Fev 47.172097 11.537288 153.514964 2006 Fev 11.908374 4.948685 96.692378 2007 Fev 9.695475 22.647016 134.718689 2008 Fev 8.733213 6.416213 98.246685 2009 Fev 10.047596 3.844021 104.897675 2010 Fev 69.862665 7.766402 128.248432 2011 Fev 6.274435 14.488818 109.018397 2012 Fev 13.034555 36.203963 89.173868 2013 Fev 8.241457 7.104568 80.173343 2014 Fev 5.907136 7.041881 129.079806 2015 Fev 46.901935 6.745321 98.318978 2016 Fev 12.211927 4.665387 102.403554 2017 Fev 14.904440 5.218321 74.389874 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Mar 0.000000 44.769512 57.736912 34.308037 4.959537 1.622504 2002 Mar 0.000617 43.605021 71.166163 42.528017 34.784582 13.964323 2003 Mar 0.000308 141.255666 131.024609 156.827253 45.210960 25.045442 2004 Mar 0.000308 220.293442 180.354866 84.740520 8.633857 3.462990 2005 Mar 0.000925 104.012774 116.048534 96.680248 19.184811 7.764200 2006 Mar 0.000154 128.668012 70.255187 87.596756 13.741502 5.892252 2007 Mar 0.000771 205.113401 105.202419 32.547799 30.962846 6.795641 2008 Mar 0.000771 103.620118 127.454073 55.862651 80.434329 68.792381 2009 Mar 0.001541 125.883892 141.947148 52.358781 26.440020 9.977727 2010 Mar 0.000000 227.919910 146.734356 73.249154 16.511949 9.132766 2011 Mar 0.000000 98.516816 141.485546 114.083357 25.596359 14.618269 2012 Mar 0.016492 159.148157 127.753656 47.260733 33.266823 15.013782 2013 Mar 0.000308 88.807005 102.169909 104.152958 6.107719 4.986616 2014 Mar 0.001233 140.983074 85.124268 18.017532 29.973154 19.959948 2015 Mar 0.001387 156.261738 85.625611 44.736618 24.295224 11.406969 2016 Mar 0.000000 86.784919 87.951962 32.514587 17.540399 8.584441 2017 Mar 0.000154 94.194538 140.553171 79.526230 45.869832 10.694595 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Mar 0.003567 145.483777 15.521933 320.159545 3.064967 0.000000 2002 Mar 1.120946 153.289352 17.100661 591.558397 2.814679 0.000000 2003 Mar 31.788821 174.533097 32.840379 586.880249 4.503062 0.318556 2004 Mar 0.242977 217.509412 16.932108 441.422056 2.536817 0.000000 2005 Mar 11.667674 178.737084 43.949049 510.079081 1.921703 0.004013 2006 Mar 0.395582 167.592705 26.106586 453.363643 2.328951 0.000000 2007 Mar 2.539570 207.076554 41.380696 526.736695 8.301925 0.005518 2008 Mar 7.724938 167.692920 22.906575 744.639888 4.976064 0.011538 2009 Mar 4.075124 145.991529 20.865577 503.819454 4.017334 0.000000 2010 Mar 0.209286 168.825340 11.843797 271.938638 2.131690 0.000000 2011 Mar 20.190513 153.826054 26.959367 573.366653 6.250836 0.015050 2012 Mar 2.638267 149.672731 19.966069 683.984957 7.778017 0.000502 2013 Mar 0.057871 143.947159 9.579174 396.828134 2.477427 0.000000 2014 Mar 41.697782 135.308692 90.566583 447.871081 2.015030 0.000000 2015 Mar 12.897623 146.950802 51.473874 477.701375 6.706869 0.028093 2016 Mar 1.627513 102.840953 29.753014 152.816333 1.628993 0.000502 2017 Mar 3.043362 111.447128 58.855179 548.839522 2.218654 0.049163 BIOME_NUM 12.0 13.0 14.0 2001 Mar 13.559701 9.062745 85.992960 2002 Mar 15.317272 14.328885 77.064729 2003 Mar 28.200050 8.927324 230.182079 2004 Mar 17.250903 5.462641 185.432485 2005 Mar 21.394398 9.696048 160.310540 2006 Mar 18.413510 8.031216 242.327365 2007 Mar 7.248840 25.590911 108.114730 2008 Mar 8.074503 9.986981 99.981726 2009 Mar 25.000604 8.004694 140.393718 2010 Mar 24.481529 5.185772 152.864324 2011 Mar 31.800186 7.309106 111.910131 2012 Mar 22.074356 9.286571 153.153497 2013 Mar 7.136525 9.044662 171.660599 2014 Mar 24.262971 9.961263 199.493545 2015 Mar 16.133829 15.651347 136.887490 2016 Mar 11.213238 6.606284 151.816070 2017 Mar 56.041908 7.587582 131.031727 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Abr 0.000000 23.675130 52.096810 137.597482 21.573224 6.919239 2002 Abr 0.000000 32.785237 50.562214 98.440487 14.291024 10.946285 2003 Abr 0.002620 68.361945 64.064831 141.184382 39.548257 7.528240 2004 Abr 0.000000 42.651253 70.943004 103.173202 39.675602 2.991071 2005 Abr 0.000617 59.959354 96.459497 232.783189 31.331040 4.752904 2006 Abr 0.000000 43.317114 51.671892 156.246042 68.094315 13.328355 2007 Abr 0.000308 75.180436 74.580793 82.199799 16.161058 16.290212 2008 Abr 0.000617 44.047907 91.161776 204.818651 103.763392 89.066942 2009 Abr 0.001233 82.919617 91.901561 281.986826 122.585547 12.108106 2010 Abr 0.000000 59.825814 74.849806 78.529869 38.862393 17.119442 2011 Abr 0.000000 36.284836 116.941168 521.844179 53.620579 22.697081 2012 Abr 0.000462 37.726821 94.356305 172.719214 47.346066 17.256523 2013 Abr 0.000154 83.766798 89.376508 170.859340 40.559404 63.565310 2014 Abr 0.000000 83.349640 89.116666 85.753487 72.750023 9.141755 2015 Abr 0.000000 62.071487 77.032480 66.739594 32.100647 12.296874 2016 Abr 0.000000 93.673856 107.751929 267.655831 24.626737 10.206945 2017 Abr 0.000308 60.064715 157.430679 259.585305 56.259529 0.991031 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Abr 2.560974 132.973701 42.203437 289.126569 0.702079 0.000000 2002 Abr 3.412783 144.868016 23.242847 426.961466 2.555907 0.000000 2003 Abr 12.111613 118.742724 74.783476 536.263449 3.637660 0.024080 2004 Abr 7.481961 107.863913 60.563242 291.702392 1.187807 0.022073 2005 Abr 5.500090 190.885832 67.004152 349.487932 1.187807 0.000502 2006 Abr 20.666955 106.652991 42.204271 387.235091 0.863281 0.000000 2007 Abr 7.538246 113.796038 34.375717 388.608231 2.163506 0.000000 2008 Abr 23.620736 131.826805 182.177863 547.002355 4.766077 0.016555 2009 Abr 15.439175 186.535978 76.559127 592.798958 1.756258 0.004013 2010 Abr 12.617386 104.058556 65.004040 171.860371 1.989577 0.005017 2011 Abr 19.048955 91.315203 106.399756 409.356858 1.639598 0.054180 2012 Abr 6.084742 172.914638 132.970281 305.414857 4.978185 0.005518 2013 Abr 8.367461 145.995426 16.242040 462.985097 1.580208 0.095316 2014 Abr 8.024201 88.906722 95.020732 628.699480 4.923037 0.011538 2015 Abr 4.339506 177.553443 108.767013 421.563603 5.130903 0.008027 2016 Abr 3.311311 130.202221 56.154986 243.812933 1.022363 0.006522 2017 Abr 9.359982 97.633710 67.168533 379.753842 1.677778 0.003010 BIOME_NUM 12.0 13.0 14.0 2001 Abr 20.769079 42.457831 64.666416 2002 Abr 18.349764 46.175674 43.448313 2003 Abr 9.224968 12.533053 172.347386 2004 Abr 60.792509 8.747299 134.646395 2005 Abr 25.131132 12.570022 87.511120 2006 Abr 23.649795 8.800744 79.125090 2007 Abr 9.255323 5.773667 87.547267 2008 Abr 23.668008 11.710482 77.968396 2009 Abr 38.812254 10.966672 198.842905 2010 Abr 32.486215 6.714379 74.426021 2011 Abr 24.876148 30.975997 82.992785 2012 Abr 27.814538 32.742897 87.294240 2013 Abr 20.857109 10.393244 117.187547 2014 Abr 51.069714 9.424804 149.683415 2015 Abr 30.343133 27.108267 232.423173 2016 Abr 9.100511 16.272997 144.478293 2017 Abr 46.762301 11.181658 108.620783 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Mai 0.000000 9.948100 58.293280 194.406676 1.519144 2.179819 2002 Mai 0.000154 21.687960 48.083014 259.369427 17.277404 18.759924 2003 Mai 0.000617 53.719126 100.271534 371.842000 42.043943 91.251255 2004 Mai 0.000771 25.977772 47.422710 90.004628 42.529100 4.678745 2005 Mai 0.000308 32.214936 89.235887 434.197605 27.980618 3.242761 2006 Mai 0.000771 18.705368 52.842710 259.469063 63.459370 6.301249 2007 Mai 0.000154 27.569223 63.820274 304.786891 41.387840 6.020345 2008 Mai 0.000617 22.302366 78.536507 332.236642 29.508067 17.622823 2009 Mai 0.000308 20.860381 63.083545 189.673961 52.255080 11.227190 2010 Mai 0.000000 23.389673 54.517927 117.969166 5.717378 2.804550 2011 Mai 0.000000 24.325677 53.622236 578.952282 23.468174 22.355501 2012 Mai 0.000308 17.802443 56.975728 396.020365 31.780900 72.657626 2013 Mai 0.000154 28.638154 80.835345 255.749314 33.235679 4.739420 2014 Mai 0.002466 21.675709 37.276640 77.998476 22.227943 6.566423 2015 Mai 0.000000 23.839911 38.441344 85.022822 4.246681 4.089969 2016 Mai 0.000000 34.980680 67.895209 157.275616 20.716413 20.085793 2017 Mai 0.000000 16.789256 54.548497 202.360960 6.187309 2.905676 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Mai 1.100731 360.944364 11.273052 95.712628 6.766259 0.000000 2002 Mai 7.086776 436.149574 22.342505 332.981836 26.929288 0.170064 2003 Mai 65.871461 489.978533 116.057365 307.128915 22.721057 1.783413 2004 Mai 24.138401 404.860466 75.526112 175.809334 20.389985 0.070735 2005 Mai 3.569747 412.360944 18.318084 403.068821 20.928740 0.254845 2006 Mai 6.422056 257.083453 96.340789 271.645070 28.734331 0.021572 2007 Mai 6.053032 373.176624 42.794208 284.902979 25.073339 0.055685 2008 Mai 20.122733 338.210767 56.065703 134.009043 4.121267 0.165047 2009 Mai 6.109713 232.606685 34.672771 359.885920 5.489367 0.137957 2010 Mai 2.485267 265.965217 18.344785 101.707096 5.934794 0.107356 2011 Mai 6.181853 313.276354 25.096935 229.522802 12.015944 0.134446 2012 Mai 7.674599 409.334474 38.753933 204.408536 22.899228 0.078259 2013 Mai 1.395237 340.854752 29.480992 400.909676 19.499129 0.312536 2014 Mai 7.249686 273.713447 30.131008 122.389433 5.296348 0.119898 2015 Mai 8.132808 304.752020 24.959255 171.244825 15.628150 0.246317 2016 Mai 15.270716 270.625179 35.902710 334.165578 14.576092 0.365211 2017 Mai 2.308484 323.876235 23.780216 126.234227 10.959643 0.007023 BIOME_NUM 12.0 13.0 14.0 2001 Mai 2.653051 42.684068 25.989465 2002 Mai 5.497340 34.714736 44.677301 2003 Mai 6.914932 14.200697 126.585685 2004 Mai 32.977970 23.333363 91.161935 2005 Mai 10.499890 22.835078 45.364088 2006 Mai 13.590056 17.035292 150.984696 2007 Mai 5.375919 10.286756 120.838362 2008 Mai 4.389373 14.379115 80.751690 2009 Mai 23.400881 12.101876 76.124915 2010 Mai 13.483813 11.582294 75.835742 2011 Mai 5.855532 33.761566 135.911529 2012 Mai 28.670557 20.209844 30.688534 2013 Mai 13.386676 19.002710 72.944007 2014 Mai 8.195924 8.763372 72.329513 2015 Mai 13.444351 17.339888 53.894705 2016 Mai 5.454843 17.217326 86.065253 2017 Mai 29.593357 31.725031 39.508325 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Jun 0.012331 46.332785 43.506737 3.371022 7.151395 3.089949 2002 Jun 0.005086 18.960196 23.932985 53.238900 17.788168 41.270484 2003 Jun 0.001541 29.766503 26.632286 85.238701 27.395108 13.573304 2004 Jun 0.003699 30.088101 29.817644 15.842143 23.683414 6.460803 2005 Jun 0.002004 24.963360 29.435523 181.204890 47.078918 13.600271 2006 Jun 0.003083 17.581919 27.378185 32.647435 84.112802 7.723749 2007 Jun 0.004778 32.542660 38.098964 65.676809 48.413965 3.961877 2008 Jun 0.002774 25.039318 32.599483 57.506647 4.985836 10.851901 2009 Jun 0.001387 30.837884 37.481457 35.719549 22.629357 5.991131 2010 Jun 0.000617 21.445996 27.992635 50.615149 78.823829 4.804590 2011 Jun 0.000462 20.926538 28.069060 197.130064 44.138908 30.991628 2012 Jun 0.011560 25.611456 26.543634 122.502610 97.373992 24.521836 2013 Jun 0.000771 32.344801 28.136313 70.774857 29.009760 13.393525 2014 Jun 0.002774 18.479943 28.582630 105.863378 25.660723 7.397900 2015 Jun 0.001695 25.143455 25.177170 7.921072 16.087696 8.759725 2016 Jun 0.002158 22.538817 27.796990 60.528943 3.630027 13.440717 2017 Jun 0.026049 16.990791 24.217283 191.616865 12.124773 13.229477 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Jun 4.647092 726.834858 40.303456 405.066977 49.474295 2002 Jun 19.215829 479.351371 30.773513 481.091611 22.337141 2003 Jun 39.553794 516.877145 24.638837 435.361298 22.731662 2004 Jun 9.640219 686.265912 114.286720 512.598083 21.582034 2005 Jun 15.339289 666.317713 47.410235 678.767023 34.444291 2006 Jun 17.903037 565.690938 96.106316 459.708499 25.525130 2007 Jun 11.965747 562.908323 68.961708 690.481332 18.854320 2008 Jun 21.725671 560.376243 61.024678 632.212826 28.662214 2009 Jun 7.481168 553.797736 22.770564 732.594131 29.531859 2010 Jun 17.815042 571.040707 99.994726 574.616684 34.297936 2011 Jun 21.578616 592.646895 57.168810 530.325801 34.722153 2012 Jun 40.369928 737.452555 33.136599 831.261378 24.464588 2013 Jun 24.323508 633.827704 13.668680 761.202803 14.569729 2014 Jun 8.870857 589.431131 31.023840 488.127773 24.297023 2015 Jun 35.863153 515.917315 41.262208 545.790204 20.739964 2016 Jun 13.304699 585.744361 13.601092 758.920549 16.287808 2017 Jun 10.505900 572.259980 34.535091 345.463210 20.595730 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Jun 0.206685 14.910511 37.437618 2.385681 2002 Jun 0.892459 17.296436 15.433147 6.940163 2003 Jun 4.474334 22.478081 7.319955 21.037370 2004 Jun 17.104708 24.208332 101.585679 36.472003 2005 Jun 2.939746 20.219648 30.920141 24.832771 2006 Jun 0.163041 14.391436 70.654286 30.363214 2007 Jun 1.069546 19.096503 22.022955 57.328640 2008 Jun 0.657179 23.625510 15.253926 20.531316 2009 Jun 0.677747 22.365766 14.906332 27.037719 2010 Jun 1.528066 19.655040 41.327449 37.881724 2011 Jun 0.349158 13.077052 23.037607 33.218802 2012 Jun 1.951971 21.169769 27.853282 17.314261 2013 Jun 0.722897 16.091331 8.076222 19.374622 2014 Jun 0.199160 18.316373 12.800278 22.447090 2015 Jun 2.782224 34.462343 18.921538 21.615717 2016 Jun 2.212335 24.126372 3.559517 25.953319 2017 Jun 1.250646 38.171758 36.515390 17.928755 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Jul 0.025586 27.403217 23.248225 0.016606 25.104973 4.184353 2002 Jul 0.011868 40.711863 40.098220 13.185180 24.835749 36.196226 2003 Jul 0.008323 56.345204 45.426511 4.965200 6.426773 16.953146 2004 Jul 0.013872 46.576587 43.531192 0.730665 10.345403 11.406969 2005 Jul 0.007090 69.533787 40.932771 6.111015 14.345007 10.678864 2006 Jul 0.008786 35.078691 49.522845 0.016606 14.244653 19.993656 2007 Jul 0.061962 54.681470 45.594644 1.046179 20.581455 30.643307 2008 Jul 0.005703 40.465611 53.279856 0.215878 22.499244 23.285857 2009 Jul 0.004932 53.434895 28.897497 2.324843 12.885383 3.687714 2010 Jul 0.002774 48.006320 49.672637 0.232484 26.135499 6.069784 2011 Jul 0.002929 47.389464 30.887582 4.732716 5.042588 4.979874 2012 Jul 0.007553 51.647422 47.019190 0.481575 9.053958 11.597984 2013 Jul 0.004624 46.532482 29.151225 0.232484 4.751909 9.779970 2014 Jul 0.001850 37.260657 27.289533 0.265696 6.905702 32.270305 2015 Jul 0.009865 49.177549 28.506206 3.121932 11.771806 29.641040 2016 Jul 0.006628 50.751236 51.726918 1.295270 8.793731 6.872047 2017 Jul 0.015876 44.176546 27.524920 0.813695 6.546505 57.333726 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Jul 32.171323 530.560284 161.060291 787.093604 26.263268 2002 Jul 57.357341 814.174340 111.809319 883.222909 69.032815 2003 Jul 34.327202 846.399895 31.670518 741.884137 24.683060 2004 Jul 28.758144 818.818713 60.663373 781.222245 29.005830 2005 Jul 26.066366 962.167370 141.452189 676.816217 32.274421 2006 Jul 36.565528 776.525525 112.990862 807.861170 32.369870 2007 Jul 18.372345 971.532946 82.919932 751.742339 57.710466 2008 Jul 39.238676 781.773410 124.001905 780.474120 31.118430 2009 Jul 22.155341 797.223660 100.269251 574.872372 22.568339 2010 Jul 26.419139 812.357679 86.846726 810.863139 33.508892 2011 Jul 34.131393 932.011239 32.047677 873.809794 25.728755 2012 Jul 85.249011 837.244212 82.343346 663.009053 20.739964 2013 Jul 64.322827 765.446286 13.370791 555.459007 25.370291 2014 Jul 68.978639 789.900227 78.223801 810.086605 30.893595 2015 Jul 51.616652 877.116669 55.152845 834.954653 26.581431 2016 Jul 39.127295 784.563820 49.426201 614.863906 18.928558 2017 Jul 25.785733 718.447484 101.440781 719.885479 26.687485 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Jul 3.521676 20.975495 42.202259 2.349534 2002 Jul 1.341447 33.026539 72.953225 16.193714 2003 Jul 16.025631 35.318362 13.975666 26.097905 2004 Jul 11.108830 35.139266 22.263659 22.627824 2005 Jul 3.118338 30.759000 100.478604 28.339000 2006 Jul 2.398953 20.250004 36.399258 17.061235 2007 Jul 1.543618 50.526355 39.236263 21.868743 2008 Jul 0.894967 21.312438 14.471941 15.868394 2009 Jul 3.174525 38.542092 23.747260 22.591677 2010 Jul 18.182281 19.472909 46.229521 14.386380 2011 Jul 1.311849 11.377157 40.657578 22.121770 2012 Jul 1.666525 36.119741 30.464854 16.880501 2013 Jul 1.603817 14.628207 8.517043 39.291445 2014 Jul 0.648149 13.708442 19.519881 33.038069 2015 Jul 3.372682 29.053033 22.235128 19.699942 2016 Jul 7.814909 19.582188 9.605633 14.422527 2017 Jul 5.416959 48.623079 102.125755 27.543773 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Ago 0.021425 82.596794 94.099520 0.000000 50.715174 17.067755 2002 Ago 0.008632 116.581438 106.217332 1.129209 54.015764 34.587205 2003 Ago 0.005857 99.114070 83.250348 0.116242 10.890772 41.454758 2004 Ago 0.000617 101.266633 117.521991 0.298908 37.110014 8.004654 2005 Ago 0.011868 154.063845 128.780796 0.398544 33.324959 12.249682 2006 Ago 0.033293 98.499664 122.617953 0.381938 24.237089 24.634198 2007 Ago 0.025586 123.725203 125.656577 0.000000 33.171314 54.048267 2008 Ago 0.008323 83.138304 58.797679 2.922660 74.969565 10.694595 2009 Ago 0.004316 62.814532 42.855603 1.627390 39.596703 20.627377 2010 Ago 0.012177 157.809696 248.363183 0.000000 36.853940 23.919577 2011 Ago 0.009402 71.233663 66.758018 0.083030 18.042858 11.042916 2012 Ago 0.031752 83.709217 113.544879 0.000000 9.152235 47.502069 2013 Ago 0.005241 69.721233 53.539698 2.773205 17.278788 36.384994 2014 Ago 0.006782 67.180302 53.408249 0.963149 11.391155 36.032178 2015 Ago 0.003545 75.837109 81.257207 0.714059 17.249720 81.147683 2016 Ago 0.010173 78.819089 110.866977 3.653324 19.042240 16.083466 2017 Ago 0.015568 64.544423 54.050211 0.631029 11.328866 88.520863 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Ago 17.046473 1240.009087 187.251154 1231.583918 54.378242 2002 Ago 46.529981 1027.067217 222.770864 974.996044 52.233826 2003 Ago 28.845742 1136.837427 49.532172 723.076847 63.884943 2004 Ago 15.795912 1029.077625 82.954978 888.279854 39.469140 2005 Ago 20.119166 1069.821389 86.905970 1124.602069 44.443083 2006 Ago 5.837008 1111.337917 154.950647 1027.279552 50.908148 2007 Ago 5.538934 1165.708589 129.941327 900.979036 33.860992 2008 Ago 4.886106 904.596523 218.920003 667.081124 53.924330 2009 Ago 11.313711 994.818835 52.012911 685.253929 51.317518 2010 Ago 21.872726 1256.384091 117.253928 1065.945295 55.977540 2011 Ago 8.148266 1067.495306 50.418329 519.511135 67.573509 2012 Ago 30.127617 1088.513013 66.778023 750.653296 44.638223 2013 Ago 40.857072 1027.416853 23.762693 415.701714 40.113949 2014 Ago 33.040175 977.326442 114.043069 537.646060 52.363212 2015 Ago 14.210811 1004.760086 138.258019 645.641193 31.228727 2016 Ago 5.492955 961.328354 62.429846 467.663244 28.235876 2017 Ago 40.606564 884.540316 113.463145 663.113222 36.071162 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Ago 3.269339 35.227296 119.378443 10.590978 2002 Ago 8.248848 23.753002 190.515342 51.545170 2003 Ago 13.101436 85.550271 24.673908 16.446741 2004 Ago 12.954449 24.323682 17.791156 33.074215 2005 Ago 5.107433 38.080692 19.368788 27.724506 2006 Ago 1.594286 16.674152 64.809895 29.531840 2007 Ago 4.182366 90.176415 48.029773 13.012806 2008 Ago 1.853144 27.347067 69.790728 16.374448 2009 Ago 2.638748 41.774929 16.531382 41.460246 2010 Ago 8.679777 21.773839 67.479332 7.410070 2011 Ago 1.060516 20.377496 153.561140 37.773284 2012 Ago 2.208823 57.608240 61.137460 34.917696 2013 Ago 2.466176 26.815850 10.589343 36.652737 2014 Ago 0.844299 27.189220 33.595606 32.025961 2015 Ago 4.995562 25.128096 44.712165 43.845927 2016 Ago 8.049688 32.039992 16.054797 31.989815 2017 Ago 0.864867 44.813492 75.250154 8.530617 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Set 0.002774 60.699336 81.425340 0.531393 34.585260 10.092336 2002 Set 0.002158 134.690165 92.635233 0.033212 46.525244 56.965179 2003 Set 0.002774 112.037411 99.085431 0.531393 23.748471 12.501372 2004 Set 0.002158 141.682626 278.455954 0.049818 34.511206 2.800056 2005 Set 0.005549 163.125559 143.059883 0.000000 27.800674 6.793394 2006 Set 0.005549 136.191569 105.954433 0.116242 26.828976 16.283470 2007 Set 0.001541 211.957620 303.079813 0.016606 21.596063 16.231784 2008 Set 0.006628 101.886551 135.065918 0.431757 41.672290 5.775396 2009 Set 0.002620 100.949323 78.496767 0.614423 35.068340 8.676577 2010 Set 0.002158 185.279683 236.126149 0.033212 29.164789 7.382169 2011 Set 0.012793 112.613225 142.717503 0.763877 37.641541 16.429541 2012 Set 0.012639 102.774162 170.019264 0.033212 20.890128 22.721801 2013 Set 0.004778 77.650309 73.419147 1.693814 19.818076 5.258532 2014 Set 0.003083 85.466674 82.036733 0.016606 33.822573 14.780070 2015 Set 0.010481 117.677322 155.248006 0.016606 43.558242 19.568929 2016 Set 0.000771 83.842144 86.081099 0.415151 24.799760 8.445112 2017 Set 0.038688 135.997998 130.547723 0.033212 14.984500 44.758194 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 \ 2001 Set 2.031418 1041.333825 59.118856 602.505641 41.957172 2002 Set 11.333530 846.622593 330.111849 554.654062 54.346426 2003 Set 13.546884 804.655102 101.769544 393.835635 32.446229 2004 Set 1.708770 1207.745116 65.342816 794.840010 37.352297 2005 Set 3.478581 930.333207 108.318094 658.491894 56.789915 2006 Set 6.130325 1066.804384 129.574181 488.118303 35.326662 2007 Set 5.014927 1253.876509 113.957958 709.506431 26.099944 2008 Set 1.744840 1183.549505 79.214261 633.197699 46.761428 2009 Set 4.239223 990.205640 31.184884 551.813082 38.663128 2010 Set 1.388103 1241.331358 96.044569 620.422757 44.856694 2011 Set 5.684800 1325.134391 54.897511 443.855829 39.252789 2012 Set 4.175803 1163.962634 32.892113 484.159870 36.450837 2013 Set 2.033796 876.446347 14.618253 345.709428 35.093342 2014 Set 3.059217 945.812965 47.732322 265.906290 30.325145 2015 Set 3.762781 932.665415 63.716526 400.275190 23.022251 2016 Set 19.959427 778.828782 33.844189 541.642372 18.355865 2017 Set 5.097770 1082.794121 100.344350 588.764766 25.892078 BIOME_NUM 11.0 12.0 13.0 14.0 2001 Set 0.028093 30.625437 147.242173 5.385856 2002 Set 0.579421 21.309403 127.365858 57.979280 2003 Set 3.122352 22.217026 38.661227 30.182481 2004 Set 0.398822 32.137129 34.109964 22.736264 2005 Set 0.349158 13.990746 22.521240 16.519034 2006 Set 0.253340 25.428614 77.414878 50.027010 2007 Set 2.121534 31.882145 144.079274 8.892084 2008 Set 0.850319 27.198326 78.636879 11.856112 2009 Set 0.140967 38.876000 16.841202 55.882772 2010 Set 0.054180 17.964252 54.040929 12.470606 2011 Set 0.025585 28.612882 392.616192 36.038243 2012 Set 0.416380 32.571210 134.570485 29.170373 2013 Set 0.272403 20.058766 22.884907 12.542899 2014 Set 0.220230 25.061315 36.647194 39.616765 2015 Set 0.428420 37.601079 25.200723 126.260365 2016 Set 0.367719 30.786320 9.806956 11.386205 2017 Set 0.137456 18.040140 134.035231 8.133004 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Out 0.001387 33.984645 63.303647 0.249090 27.503074 2.988824 2002 Out 0.000925 92.179191 123.950790 0.182666 18.431815 3.498946 2003 Out 0.002466 87.248021 94.628375 0.000000 15.073780 3.750636 2004 Out 0.001695 76.937281 63.581831 0.116242 24.932642 2.422520 2005 Out 0.002466 70.848358 119.429538 0.464969 46.940500 2.222516 2006 Out 0.002620 97.066869 63.749964 0.149454 14.935362 2.831517 2007 Out 0.001079 93.341844 134.423955 0.016606 16.076623 14.937376 2008 Out 0.000000 65.881658 76.124561 0.132848 19.790393 3.303437 2009 Out 0.000617 70.221701 53.032242 0.016606 16.844846 2.564096 2010 Out 0.002929 87.004219 124.308455 0.298908 26.783298 2.777583 2011 Out 0.000617 54.040112 41.571677 0.282302 39.491505 11.624951 2012 Out 0.003237 76.394546 104.805014 0.016606 15.914673 3.986596 2013 Out 0.001387 42.950186 62.710595 0.016606 19.587609 1.950601 2014 Out 0.003083 86.824124 71.872322 0.747271 45.635213 3.018038 2015 Out 0.001079 98.924786 122.880852 0.066424 37.222133 3.355123 2016 Out 0.000308 61.889555 118.069188 0.315514 18.074003 3.098938 2017 Out 0.012485 63.255581 88.040614 0.365332 17.111302 3.964124 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Out 0.386861 815.557296 13.036188 485.267852 7.249867 0.011538 2002 Out 0.925138 741.187762 74.974558 524.568080 16.578396 0.000000 2003 Out 0.831593 760.323669 173.502369 314.638573 30.486347 0.142974 2004 Out 0.181936 1104.017267 58.715830 420.152583 17.032308 0.006522 2005 Out 3.848794 675.623718 129.482394 690.765430 19.991221 0.001505 2006 Out 0.407076 630.683761 29.124694 266.332437 12.764686 0.003512 2007 Out 2.075416 787.905408 54.210781 331.447707 7.572272 0.002508 2008 Out 1.806674 738.215297 43.808032 511.007134 15.184844 0.001003 2009 Out 0.068573 631.543933 35.360335 380.965994 12.334106 0.000000 2010 Out 0.286182 637.623040 35.907717 411.459183 28.927350 0.000000 2011 Out 2.225245 733.761331 31.554533 344.061659 19.815171 0.001003 2012 Out 0.227519 672.721959 24.015523 230.536085 7.205324 0.001003 2013 Out 0.265174 503.359359 23.729316 288.700422 21.229934 0.001003 2014 Out 0.998070 738.201935 46.927104 302.583346 23.230117 0.000000 2015 Out 0.590598 558.773372 42.068260 398.447493 20.385743 0.135951 2016 Out 0.199773 642.144371 7.717577 592.751609 7.661357 0.007023 2017 Out 0.120894 723.049544 23.725978 368.456211 28.800085 0.000000 BIOME_NUM 12.0 13.0 14.0 2001 Out 16.470772 85.292188 7.373923 2002 Out 30.959345 244.736296 73.305474 2003 Out 50.107452 48.849129 11.133178 2004 Out 15.651180 127.542267 58.666067 2005 Out 22.414335 35.092467 16.374448 2006 Out 13.693264 95.253451 104.030154 2007 Out 40.296627 74.290154 9.578871 2008 Out 14.418755 50.108904 13.952620 2009 Out 18.689743 34.821626 16.663621 2010 Out 21.066561 98.507569 15.217754 2011 Out 20.386603 376.927072 9.795751 2012 Out 10.688092 327.186255 18.145635 2013 Out 26.557830 47.790275 18.687835 2014 Out 22.948588 115.005999 39.580618 2015 Out 28.330578 99.578076 85.089292 2016 Out 20.829790 20.329995 16.410594 2017 Out 66.311098 105.064426 7.554657 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Nov 0.001695 32.765022 30.459607 0.730665 5.721531 0.242701 2002 Nov 0.000000 42.109743 54.890877 0.780483 12.106087 3.285459 2003 Nov 0.000000 45.118676 62.031949 0.116242 4.203079 1.973073 2004 Nov 0.000000 56.242293 83.302317 0.963149 4.391329 2.510162 2005 Nov 0.000000 73.544268 83.990134 0.415151 5.777590 2.312406 2006 Nov 0.000308 46.163103 38.636990 0.348726 10.610474 0.573045 2007 Nov 0.000771 40.735141 77.689728 0.083030 12.010578 1.471939 2008 Nov 0.000000 40.598538 55.401390 1.643996 8.755666 1.759586 2009 Nov 0.000000 56.155921 48.694407 1.295270 6.261363 1.793294 2010 Nov 0.001233 39.582288 39.911745 2.275025 15.320165 1.426995 2011 Nov 0.001079 40.401904 43.405857 0.265696 12.699210 1.561829 2012 Nov 0.001695 36.463094 43.989737 0.199272 5.424623 1.197777 2013 Nov 0.000000 32.659048 41.565563 0.863513 7.188768 2.555107 2014 Nov 0.000000 33.488465 46.829658 0.498181 22.663270 0.878669 2015 Nov 0.001541 68.564705 84.552616 0.464969 13.839779 0.793274 2016 Nov 0.000154 36.569680 65.409896 1.378300 7.731368 0.961817 2017 Nov 0.000154 34.774244 41.522766 0.298908 8.023431 3.116916 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Nov 0.000396 800.815227 2.544155 123.222787 0.434822 0.0 2002 Nov 0.116930 684.890193 18.111982 972.666440 6.429007 0.0 2003 Nov 0.000000 769.524448 14.947016 299.770776 4.019455 0.0 2004 Nov 0.088391 981.341135 3.433650 617.970044 2.791347 0.0 2005 Nov 0.099490 1107.032602 8.068869 403.646487 5.953884 0.0 2006 Nov 0.002775 1009.874352 3.816650 386.373327 1.387189 0.0 2007 Nov 0.091959 805.529750 4.673602 350.273936 2.861343 0.0 2008 Nov 0.000000 888.503232 9.998889 347.859103 2.131690 0.0 2009 Nov 0.156568 825.063173 6.930716 1548.362697 1.295983 0.0 2010 Nov 0.000000 618.229916 5.807583 211.757205 6.422644 0.0 2011 Nov 0.004756 671.572836 7.554864 484.699656 5.035455 0.0 2012 Nov 0.000000 734.521846 6.865631 303.776558 3.033151 0.0 2013 Nov 0.000000 583.802989 7.564043 344.904484 5.300590 0.0 2014 Nov 0.000000 598.366900 12.508832 283.349909 1.667172 0.0 2015 Nov 0.000000 727.556957 9.015938 1035.272177 8.745231 0.0 2016 Nov 0.177972 744.282711 5.754180 735.075254 3.260107 0.0 2017 Nov 0.002775 641.145570 6.818069 280.262711 8.420705 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Nov 5.075402 103.090578 2.927881 2002 Nov 63.445560 226.732565 11.458499 2003 Nov 13.693264 23.873438 9.181258 2004 Nov 10.023312 86.982738 20.314436 2005 Nov 26.433373 26.898907 7.988417 2006 Nov 28.974110 195.605475 23.748371 2007 Nov 21.461179 47.199969 13.121246 2008 Nov 7.151703 35.859584 11.711525 2009 Nov 21.324580 44.666757 9.759604 2010 Nov 9.983850 40.265381 4.735216 2011 Nov 12.849388 77.105057 5.855763 2012 Nov 11.662497 151.177011 11.892259 2013 Nov 12.217998 52.933452 5.205122 2014 Nov 6.201583 93.218121 9.181258 2015 Nov 57.073988 91.097199 14.747847 2016 Nov 25.817161 46.270509 13.771886 2017 Nov 36.271518 130.560101 7.988417 , BIOME_NUM 0.0 1.0 2.0 3.0 4.0 5.0 \ 2001 Dez 0.000000 27.441196 17.546983 0.232484 12.718589 0.168543 2002 Dez 0.001233 49.350906 36.506285 0.448363 5.864102 0.294388 2003 Dez 0.000000 63.737671 72.798582 1.594178 2.405022 0.525853 2004 Dez 0.000000 56.472618 202.523981 3.487264 1.293521 0.660687 2005 Dez 0.001079 28.440907 25.216911 1.976116 2.447932 1.379803 2006 Dez 0.000000 59.290430 41.220126 0.464969 22.976788 0.869680 2007 Dez 0.000925 46.620079 36.151677 1.660602 1.305979 0.361805 2008 Dez 0.000771 38.321012 33.427921 0.315514 1.549596 0.083148 2009 Dez 0.000000 50.198700 36.604108 0.315514 3.565662 0.188768 2010 Dez 0.000000 54.009483 29.047289 1.843268 3.798205 0.404502 2011 Dez 0.000000 63.150832 31.795501 3.570294 2.027140 0.979795 2012 Dez 0.000925 43.851885 85.674522 1.942904 3.400252 0.301130 2013 Dez 0.000000 37.214714 26.277678 0.581211 3.786440 1.977567 2014 Dez 0.000000 33.865806 40.862461 1.594178 1.921250 0.328096 2015 Dez 0.000000 63.341340 144.420233 1.345088 1.745458 1.155079 2016 Dez 0.000000 32.838530 21.404874 3.038902 3.268754 1.341600 2017 Dez 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 BIOME_NUM 6.0 7.0 8.0 9.0 10.0 11.0 \ 2001 Dez 0.000000 1148.667716 18.591775 493.478286 3.586754 0.0 2002 Dez 0.000000 1334.297312 6.571080 2094.616927 4.397008 0.0 2003 Dez 0.001585 1379.184378 13.287349 1189.565819 7.075938 0.0 2004 Dez 0.000000 1051.410922 9.673464 1363.433816 5.069392 0.0 2005 Dez 0.001585 1240.612042 5.959448 1131.022684 5.147872 0.0 2006 Dez 0.012288 1278.806468 2.261286 1009.419256 17.602880 0.0 2007 Dez 0.000000 1161.896551 2.668484 1172.624106 4.078845 0.0 2008 Dez 0.000000 1055.165616 2.281312 1175.985933 4.920916 0.0 2009 Dez 0.000000 1115.934409 0.766000 1142.916922 1.158112 0.0 2010 Dez 0.000000 1062.367121 3.326844 744.942926 2.801953 0.0 2011 Dez 0.000000 1131.710912 7.888633 1153.684237 7.235019 0.0 2012 Dez 0.004756 1018.605795 2.444024 1107.186859 3.991881 0.0 2013 Dez 0.000000 892.228974 5.115846 914.284294 2.473184 0.0 2014 Dez 0.000000 923.814272 6.963258 778.135046 3.567664 0.0 2015 Dez 0.000000 806.393262 3.884238 1914.820751 5.898736 0.0 2016 Dez 0.000000 1010.840306 42.863465 1370.735136 4.015213 0.0 2017 Dez 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 BIOME_NUM 12.0 13.0 14.0 2001 Dez 4.198134 95.979580 3.036321 2002 Dez 72.807126 60.272696 15.868394 2003 Dez 12.639936 11.206974 28.953493 2004 Dez 71.608093 42.905483 24.688185 2005 Dez 25.841445 7.581956 10.916298 2006 Dez 37.106288 35.691614 30.941561 2007 Dez 16.874497 44.220310 9.578871 2008 Dez 9.804754 4.761829 14.603260 2009 Dez 16.807716 10.875856 11.675379 2010 Dez 3.241943 6.160239 12.398312 2011 Dez 5.852497 48.275299 16.627474 2012 Dez 6.389785 20.822251 18.615542 2013 Dez 9.404064 11.012884 27.001572 2014 Dez 3.584958 17.314974 15.434634 2015 Dez 7.066708 18.853225 32.387428 2016 Dez 41.022118 12.427770 22.916997 2017 Dez 0.000000 0.000000 0.000000 ]
%%HTML
<h1 id="varintermonbio">Variação inter-anual por mês em cada bioma</h1>
labels = years[::2]
for b in range(15):
fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
fig.suptitle(biome_names_dict[b],fontsize=15)
for i in range(4):
for j in range(3):
data = intra_biome[i*3 + j][b]
slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i][j].plot(years, data, 'o', label='_nolegend_')
ax[i][j].set_xticks(labels)
ax[i][j].set_xticklabels(labels,fontsize=15)
labels_y = ax[i][j].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
ax[i][j].legend(fontsize=15)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
calendar.month_name[3]
'March'
sns.set_context("paper")
labels = years[::2]
for b in range(15):
fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
fig.suptitle(biome_names_dict[b],fontsize=15)
for i in range(4):
for j in range(3):
data = intra_biome[i*3 + j][b]
slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i][j].plot(years, data, 'o', label='_nolegend_')
ax[i][j].set_xticks(labels)
ax[i][j].set_xticklabels(labels,fontsize=15)
labels_y = ax[i][j].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
ax[i][j].legend(fontsize=15)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
sns.set_style("whitegrid")
for b in range(15):
fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
fig.suptitle(biome_names_dict[b],fontsize=15)
for i in range(4):
for j in range(3):
data = intra_biome[i*3 + j][b]
slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i][j].plot(years, data, 'o', label='_nolegend_')
ax[i][j].set_xticks(labels)
ax[i][j].set_xticklabels(labels,fontsize=15)
labels_y = ax[i][j].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
ax[i][j].legend(fontsize=15)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
labels = years[::2]
for b in range(15):
fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
fig.suptitle(biome_names_dict[b],fontsize=15)
for i in range(4):
for j in range(3):
data = intra_biome[i*3 + j][b]
#slope, intercept, r_value, p_value, std_err = stats.linregress(years_float, data)
#ax[i][j].plot(years, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
sns.regplot(years_float, data, ax=ax[i][j])
#ax[i][j].set_xticks(labels)
#ax[i][j].set_xticklabels(labels,fontsize=15)
#labels_y = ax[i][j].get_yticklabels()
#plt.setp(labels_y,fontsize=15)
ax[i][j].set_title(calendar.month_name[i*3 + j +1],fontsize=15)
ax[i][j].set_ylabel('')
#ax[i][j].legend(fontsize=15)
#fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
x = np.linspace(1,200,10)
y = 4*x
df = pd.DataFrame(y, index=x)
df
| 0 | |
|---|---|
| 1.000000 | 4.000000 |
| 23.111111 | 92.444444 |
| 45.222222 | 180.888889 |
| 67.333333 | 269.333333 |
| 89.444444 | 357.777778 |
| 111.555556 | 446.222222 |
| 133.666667 | 534.666667 |
| 155.777778 | 623.111111 |
| 177.888889 | 711.555556 |
| 200.000000 | 800.000000 |
df.columns = ['y']
df.index.name= 'x'
df
| y | |
|---|---|
| x | |
| 1.000000 | 4.000000 |
| 23.111111 | 92.444444 |
| 45.222222 | 180.888889 |
| 67.333333 | 269.333333 |
| 89.444444 | 357.777778 |
| 111.555556 | 446.222222 |
| 133.666667 | 534.666667 |
| 155.777778 | 623.111111 |
| 177.888889 | 711.555556 |
| 200.000000 | 800.000000 |
df =pd.DataFrame(dict(zip(['x','y'],[x,y])))
df
| x | y | |
|---|---|---|
| 0 | 1.000000 | 4.000000 |
| 1 | 23.111111 | 92.444444 |
| 2 | 45.222222 | 180.888889 |
| 3 | 67.333333 | 269.333333 |
| 4 | 89.444444 | 357.777778 |
| 5 | 111.555556 | 446.222222 |
| 6 | 133.666667 | 534.666667 |
| 7 | 155.777778 | 623.111111 |
| 8 | 177.888889 | 711.555556 |
| 9 | 200.000000 | 800.000000 |
ax= sns.regplot( x = 'x', y='y',data=df);ax.set(ylabel=''); plt.show()
pwd
'C:\\Users\\alpha\\Documents\\MODIS\\MCD64A1'
cd ../MODIS/MCD64A1
C:\Users\alpha\Documents\MODIS\MCD64A1
import datetime
from pyhdf.SD import SD, SDC
#import matplotlib.path as mplPath
import subprocess
import re
DATADIR = 'C:/Users/alpha/Documents/dados/MCD64A1/'
NECOS = 847
def get_tilehv(fn):
pattern = 'MCD64A1\.A\d{4}\d{3}\.h(\d{2})v(\d{2}).{22}'
# MCD64A1.A2005032.h14v04.006.2017017093958.hdf
matches= re.findall(pattern, fn)
if matches:
h, v = matches[0]
return int(h), int(v)
def get_coord(h,v):
lon_f = 'lon_coordh{}v{}.npy'.format(h,v)
lat_f = 'lat_coordh{}v{}.npy'.format(h,v)
lon = np.load(lon_f)
lat = np.load(lat_f)
return lon, lat
def get_julian(yr, mo):
'''
Return the first julian day of given month and year.
'''
dt = datetime.datetime(yr, mo, 1)
tt = dt.timetuple()
return tt.tm_yday
def julian2month(yr,jul):
# month from 1 to 12
values = [get_julian(yr,i) for i in range(1,13)]
#return month from 0 to 11
idx = [i for i in range(12) if values[i] == jul]
return idx[0]
def get_month(fn):
pattern = 'MCD64A1\.A(\d{4})(\d{3}).{29}'
matches= re.findall(pattern, fn)
year, julian = matches[0]
year, julian = int(year), int(julian)
return julian2month(year,julian)
file_ = DATADIR + 'MCD64A1.A2001152.h17v05.006.2017012103748.hdf'
h , v = get_tilehv(file_)
print('h: {}; v: {}'.format(h,v))
lon, lat = get_coord(h,v)
fich = SD(file_, SDC.READ)
# select sds
sds_obj = fich.select('Burn Date')
# get sds data
data = sds_obj.get()
attrs = sds_obj.attributes(full=1)
vra=attrs["valid_range"]
valid_range = vra[0]
fva=attrs["_FillValue"]
_FillValue = fva[0]
lat = lat.reshape(data.shape)
lon = lon.reshape(data.shape)
# Mask the data. logical_or from numpy
data = data.astype('double')
#invalid = logical_or(data < valid_range[0], data > valid_range[1])
invalid = np.logical_or(data < 1, data > valid_range[1])
invalid = np.logical_or(invalid, data == _FillValue)
# nan from numpy
data[invalid] = np.nan
# ma, isnan from numpy
data = np.ma.masked_array(data, np.isnan(data))
idx =np.logical_not(data.mask)
lat_val =lat[idx]
lon_val = lon[idx]
data_val = data[idx]
result = data_val.size
print('Result: {}'.format(result))
h: 17; v: 5 Result: 361
import pickle
from mpl_toolkits.basemap import Basemap
import matplotlib.path as mplPath
m = Basemap(projection='cyl')
shpf = m.readshapefile('ecoregions2017/ecoregions2017','ecos')
rings =[mplPath.Path(eco,closed=True) for eco in m.ecos]
ecos_info =m.ecos_info
result = np.zeros((NECOS),dtype=int)
br_vh = []
with open('bounded_rings.txt', 'rb') as f:
br_string = f.read()
br = pickle.loads(br_string)
br_vh = br[v,h]
not_included = []
for i, px in enumerate(data_val):
pt = ( lon_val[i], lat_val[i])
included = False
for ring_idx in br_vh:
ring = rings[ring_idx]
if ring.contains_point(pt):
ecoid = int(ecos_info[ring_idx]['ECO_ID'])
result[ecoid] += 1
included = True
break
if not included:
not_included.append(pt)
result.sum()
361
len(not_included)
0
total_ecoreg = df_finall.iloc[:12*17].sum() / df_finall.iloc[12*17]
total_ecoreg
Eco_Id
0 0.745548
1 57379.8
2 72840
3 4467.17
4 6082.29
5 2419.18
6 13492.1
7 12388.9
8 12075.4
9 35355.6
10 2430.83
11 21054.4
12 1371.78
13 0
14 53693.8
15 6429.86
16 24463.8
17 14375.2
18 114995
19 93857.4
20 1280.95
21 0
22 7491.87
23 23056.3
24 26488.9
25 48026.2
26 11040.9
27 35.7731
28 133877
29 11898.1
...
817 4812.59
818 9274.02
819 613
820 110.9
821 0
822 10.3596
823 2934.95
824 15.616
825 9.89902
826 696.044
827 1126.85
828 28606.5
829 4311.29
830 686.555
831 825.871
832 0
833 3.43063
834 484.523
835 10.9507
836 1.90378
837 11.3111
838 163.431
839 0
840 0.912961
841 1319.52
842 1.27572
843 39.5389
844 0
845 0.364836
846 0
Length: 847, dtype: object
ecos = m.ecos
cd -
C:\Users\alpha\Documents\TestEco2017
fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
spine.set_visible(False)
cmap = plt.get_cmap('OrRd')
min_val = 0
max_val = 1
norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))
ecos_info, ecos = get_ecos()
total_trans = np.log1p(total_ecoreg.values.astype('float'))/np.log1p(total_ecoreg.max())
patches = []
for info, ec in zip(ecos_info,ecos):
patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[info['ECO_ID']] ))))
ax.add_collection(PatchCollection(patches, match_original= True, zorder=2))
title = 'Index Burned Area 2001-2017'
cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal")
cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig('totalecologstd', dpi=1200, bbox_inches='tight')
#total_ecoreg.values.astype('float')
#np.log1p(total_ecoreg.values.astype('float'))
#total_ecoreg.values
pwd
'C:\\Users\\alpha\\Documents\\MODIS\\MCD64A1'
np.log1p(total_ecoreg.max())
13.000021174830106
total_ecoreg.max()
442421.76013651653
total_ecoreg.size
847
areas_biome
0.0 6487.848621 1.0 1632.472566 2.0 327.121754 3.0 60.219125 4.0 1444.893009 5.0 444.991141 6.0 2522.867989 7.0 1796.152160 8.0 1198.433201 9.0 105.597352 10.0 471.456944 11.0 1993.369285 12.0 329.432038 13.0 2488.539746 14.0 27.665056 Name: AREAS, dtype: float64
df_finall.iloc[:12*17].groupby(df_finall.loc['Atributos'].loc['BIOME_NUM'], axis=1).sum().sum()/areas_biome
BIOME_NUM 0.0 0.745548 1.0 13398.214739 2.0 20490.636632 3.0 10292.195324 4.0 4456.835183 5.0 2389.240821 6.0 1974.625316 7.0 132496.632098 8.0 9704.680237 9.0 130837.334430 10.0 3543.434924 11.0 219.248387 12.0 4926.673229 13.0 8993.184069 14.0 10938.998465 dtype: float64
total_biome_area
BIOME_NUM 0.0 0.745548 1.0 13398.214739 2.0 20490.636632 3.0 10292.195324 4.0 4456.835183 5.0 2389.240821 6.0 1974.625316 7.0 132496.632098 8.0 9704.680237 9.0 130837.334430 10.0 3543.434924 11.0 219.248387 12.0 4926.673229 13.0 8993.184069 14.0 10938.998465 dtype: float64
fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
spine.set_visible(False)
cmap = plt.get_cmap('OrRd')
min_val = 0
max_val = int(total_biome_area.max()) +1
norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))
ecos_info, ecos = get_ecos()
#total_trans = np.log(total_pa + 1)/np.log(total_pa.max() + 1)
patches = []
for info, ec in zip(ecos_info,ecos):
patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_biome_area[info['BIOME_NUM']] )))) if info['BIOME_NAME'] \
!= 'N/A' else patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_biome_area[0] ))))
ax.add_collection(PatchCollection(patches, match_original= True, zorder=2))
title = 'Biomes Burned Px / Area 2001-2017'
cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal")
cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig('biomesnotransform2', dpi=1200, bbox_inches='tight')
df_finall[:][:12*17].T
| 2001 | ... | 2017 | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jan | Fev | Mar | Abr | Mai | Jun | Jul | Ago | Set | Out | ... | Mar | Abr | Mai | Jun | Jul | Ago | Set | Out | Nov | Dez | |
| Eco_Id | |||||||||||||||||||||
| 0 | 36 | 0 | 0 | 0 | 0 | 80 | 166 | 139 | 18 | 9 | ... | 1 | 2 | 0 | 169 | 103 | 101 | 251 | 81 | 1 | 0 |
| 1 | 1656 | 965 | 47 | 519 | 3407 | 28944 | 4019 | 5190 | 4374 | 707 | ... | 2 | 293 | 4934 | 6861 | 5562 | 3482 | 4775 | 2597 | 32 | 0 |
| 2 | 1332 | 1335 | 1294 | 363 | 0 | 0 | 0 | 0 | 0 | 195 | ... | 842 | 76 | 0 | 0 | 0 | 0 | 0 | 34 | 2460 | 0 |
| 3 | 0 | 0 | 24 | 114 | 36 | 34 | 343 | 616 | 181 | 0 | ... | 621 | 383 | 886 | 2132 | 1804 | 1102 | 1670 | 23 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 5 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 6 | 0 | 0 | ... | 69 | 23 | 55 | 14 | 606 | 630 | 0 | 0 | 0 | 0 |
| 6 | 378 | 91 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0 |
| 7 | 1678 | 185 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 138 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 457 | 299 | 232 | 0 | 7 | 0 | 0 | 4 | 1 | 81 | ... | 45 | 5 | 0 | 0 | 1 | 0 | 2 | 0 | 26 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 42 | 103 | 674 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 53 | 284 | 137 | 0 |
| 10 | 0 | 0 | 0 | 1 | 6 | 0 | 145 | 165 | 72 | 0 | ... | 131 | 86 | 4 | 8 | 302 | 49 | 117 | 11 | 0 | 0 |
| 11 | 5962 | 22554 | 806 | 0 | 0 | 0 | 0 | 0 | 0 | 87 | ... | 462 | 13 | 0 | 0 | 0 | 0 | 0 | 70 | 510 | 0 |
| 12 | 11 | 137 | 328 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 65 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 1442 | 1692 | 497 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 126 | 36 | 5 | 0 | 0 | 0 | 0 | 200 | 1728 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 504 | 0 | 0 | 0 | 0 | 4 | 0 |
| 16 | 0 | 0 | 0 | 0 | 12 | 582 | 128 | 384 | 23 | 0 | ... | 2 | 1 | 43 | 41 | 333 | 126 | 72 | 0 | 2 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 53 | 1125 | 1289 | ... | 0 | 0 | 0 | 0 | 0 | 15 | 432 | 1265 | 3564 | 0 |
| 18 | 0 | 0 | 3 | 694 | 5225 | 17107 | 11941 | 25878 | 29019 | 20591 | ... | 0 | 137 | 2869 | 7230 | 15259 | 11598 | 20766 | 13980 | 4770 | 0 |
| 19 | 8 | 3 | 7 | 276 | 1613 | 1765 | 1338 | 1486 | 1311 | 97 | ... | 134 | 961 | 116 | 1704 | 1525 | 2609 | 895 | 448 | 269 | 0 |
| 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 |
| 23 | 174 | 830 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | ... | 34 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 358 | 0 |
| 24 | 8155 | 9762 | 481 | 30 | 12 | 0 | 0 | 0 | 37 | 120 | ... | 6263 | 82 | 23 | 21 | 32 | 7 | 242 | 392 | 6806 | 0 |
| 25 | 0 | 1572 | 449 | 25 | 137 | 5473 | 8527 | 3704 | 2488 | 1036 | ... | 4 | 0 | 42 | 61 | 1007 | 3969 | 4300 | 1919 | 35 | 0 |
| 26 | 303 | 1196 | 701 | 48 | 1 | 13 | 471 | 3563 | 952 | 0 | ... | 2137 | 965 | 118 | 70 | 3679 | 3448 | 1244 | 10 | 193 | 0 |
| 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 28 | 12 | 0 | 0 | 3 | 726 | 14960 | 6347 | 21252 | 18441 | 8886 | ... | 0 | 0 | 50 | 625 | 5599 | 12056 | 20484 | 42922 | 6511 | 0 |
| 29 | 177 | 104 | 243 | 157 | 0 | 38 | 538 | 1259 | 106 | 0 | ... | 2069 | 583 | 332 | 55 | 1553 | 161 | 125 | 20 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 817 | 0 | 3 | 945 | 1354 | 941 | 6647 | 5439 | 2406 | 1678 | 600 | ... | 669 | 521 | 0 | 1079 | 5259 | 3139 | 3207 | 1726 | 188 | 0 |
| 818 | 0 | 270 | 2784 | 646 | 640 | 172 | 88 | 245 | 414 | 256 | ... | 3509 | 1330 | 0 | 0 | 0 | 47 | 131 | 1375 | 263 | 0 |
| 819 | 0 | 519 | 255 | 16 | 2 | 94 | 183 | 50 | 7 | 41 | ... | 116 | 38 | 39 | 3 | 57 | 13 | 20 | 88 | 8 | 0 |
| 820 | 1 | 24 | 0 | 0 | 0 | 81 | 7 | 2 | 2 | 0 | ... | 1 | 0 | 54 | 103 | 133 | 2 | 0 | 6 | 1 | 0 |
| 821 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 822 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 335 | 141 | 0 | 0 | 0 | 0 | 0 | 0 |
| 823 | 3 | 4 | 14 | 175 | 305 | 0 | 0 | 0 | 0 | 0 | ... | 30 | 35 | 5 | 0 | 0 | 0 | 0 | 61 | 0 | 0 |
| 824 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 4 | 248 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 825 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 21 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 826 | 0 | 0 | 0 | 1 | 125 | 0 | 62 | 16 | 247 | 249 | ... | 0 | 0 | 0 | 22 | 1 | 0 | 0 | 0 | 0 | 0 |
| 827 | 0 | 0 | 0 | 61 | 18 | 0 | 0 | 145 | 162 | 219 | ... | 3 | 20 | 20 | 510 | 1407 | 659 | 204 | 5 | 34 | 0 |
| 828 | 0 | 0 | 127 | 183 | 139 | 4836 | 33039 | 35184 | 2755 | 28 | ... | 6 | 215 | 124 | 57569 | 149284 | 73794 | 37386 | 1132 | 1 | 0 |
| 829 | 0 | 2 | 1 | 0 | 0 | 8 | 18 | 54 | 0 | 0 | ... | 4 | 0 | 26 | 8 | 195 | 7 | 7 | 14 | 2 | 0 |
| 830 | 106 | 25 | 2 | 120 | 93 | 80 | 63 | 0 | 10 | 44 | ... | 7 | 137 | 591 | 380 | 193 | 82 | 115 | 29 | 14 | 0 |
| 831 | 0 | 0 | 0 | 0 | 97 | 579 | 466 | 368 | 108 | 64 | ... | 0 | 124 | 473 | 105 | 42 | 76 | 39 | 18 | 0 | 0 |
| 832 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 833 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 5 | 0 | 65 | 0 | 0 | 0 | 12 | 0 |
| 834 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 835 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 836 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 837 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 7 | 6 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 838 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 839 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 840 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 841 | 107 | 13 | 26 | 41 | 84 | 187 | 23 | 2 | 0 | 55 | ... | 57 | 221 | 719 | 197 | 15 | 8 | 2 | 206 | 323 | 0 |
| 842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | ... | 0 | 1 | 1 | 4 | 0 | 0 | 0 | 0 | 3 | 0 |
| 843 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | ... | 0 | 18 | 0 | 0 | 5 | 0 | 50 | 0 | 0 | 0 |
| 844 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 845 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 846 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
847 rows × 204 columns
df_finall.T['Atributos']['BIOME_NUM']
Eco_Id
0 0
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
..
817 13
818 13
819 13
820 13
821 13
822 13
823 13
824 13
825 13
826 13
827 13
828 13
829 13
830 13
831 13
832 13
833 13
834 13
835 13
836 13
837 13
838 13
839 13
840 13
841 13
842 13
843 13
844 13
845 13
846 13
Name: BIOME_NUM, Length: 847, dtype: object
lala = pd.concat([df_finall[:][:12*17].T,df_finall.T['Atributos']['BIOME_NUM']], axis=1)
start = datetime.datetime.now()
fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
spine.set_visible(False)
cmap = plt.get_cmap('OrRd')
min_val = 0
max_val = 1
norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))
ecos_info, ecos = get_ecos()
total_trans = np.log1p(total_biome_area)/np.log1p(total_biome_area.max())
patches = []
for info, ec in zip(ecos_info,ecos):
patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[info['BIOME_NUM']] )))) if info['BIOME_NAME'] \
!= 'N/A' else patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[0] )))) #cuidado estou a eliminar rock
ax.add_collection(PatchCollection(patches, match_original= True, zorder=2))
title = 'Biomes Index Burned 2001-2017'
cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal")
cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig('biomeslogtransform2', dpi=1200, bbox_inches='tight')
print('It took {} minutes.'.format((datetime.datetime.now()-start)/60))
It took 0:00:10.210871 minutes.
total_trans
BIOME_NUM 0.0 0.047232 1.0 0.805723 2.0 0.841742 3.0 0.783363 4.0 0.712412 5.0 0.659567 6.0 0.643415 7.0 1.000000 8.0 0.778380 9.0 0.998931 10.0 0.692972 11.0 0.457403 12.0 0.720908 13.0 0.771925 14.0 0.788530 dtype: float64
import time
start = datetime.datetime.now()
time.sleep(29)
datetime.datetime.now()-start
datetime.timedelta(0, 29, 10693)
print(str(datetime.timedelta(0, 29, 10693)).format('%m:%s'))
0:00:29.010693
datetime.timedelta(0, 29, 10693).seconds
29
len([ df_finall[n*12: (n+1) *12].sum() for n in range(17)])
17
n= 1
df_finall[n*12: (n+1) *12].sum()
Eco_Id
0 221.0
1 52854.0
2 15594.0
3 3825.0
4 0.0
5 108.0
6 1301.0
7 2866.0
8 4505.0
9 641.0
10 778.0
11 22253.0
12 577.0
13 0.0
14 12427.0
15 16.0
16 1430.0
17 2847.0
18 79840.0
19 13759.0
20 16.0
21 0.0
22 430.0
23 5164.0
24 61463.0
25 18019.0
26 10136.0
27 0.0
28 84137.0
29 1697.0
...
817 32739.0
818 1279.0
819 894.0
820 258.0
821 0.0
822 0.0
823 460.0
824 10.0
825 0.0
826 5482.0
827 3801.0
828 381008.0
829 210.0
830 389.0
831 3347.0
832 0.0
833 40.0
834 0.0
835 0.0
836 0.0
837 67.0
838 0.0
839 0.0
840 3.0
841 2556.0
842 3.0
843 4.0
844 0.0
845 15.0
846 0.0
Length: 847, dtype: float64
len(areas)
847
intereco =[ df_finall[n*12: (n+1) *12].sum()/areas for n in range(17)]
intereco_max= max(map(max,intereco))
for yr in range(17):
fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
spine.set_visible(False)
cmap = plt.get_cmap('OrRd')
min_val = 0
max_val = 1
norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))
ecos_info, ecos = get_ecos()
total_trans = np.log1p(intereco[yr])/np.log1p(intereco_max)
patches = []
for info, ec in zip(ecos_info,ecos):
patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans[info['ECO_ID']] ))))
ax.add_collection(PatchCollection(patches, match_original= True, zorder=2))
title = 'Index Burned Area ' + years[yr]
cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal")
cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig('ecologstd' + years[yr], dpi=1200, bbox_inches='tight')
annual_biome
[0.0 0.070748 1.0 476.112748 2.0 946.678098 3.0 382.337005 4.0 193.003218 5.0 49.000975 6.0 60.003140 7.0 8077.196531 8.0 708.472528 9.0 6661.132970 10.0 210.577024 11.0 7.037331 12.0 264.166778 13.0 881.544288 14.0 280.317529 dtype: float64, 0.0 0.034064 1.0 674.108112 2.0 1075.498635 3.0 475.613019 4.0 259.586694 5.0 222.334763 6.0 147.100443 7.0 7791.927270 8.0 896.827624 9.0 10757.476237 10.0 269.159255 11.0 11.232239 12.0 338.886287 13.0 1126.610899 14.0 501.499084 dtype: float64, 0.0 0.027436 1.0 876.820248 2.0 1272.244951 3.0 772.644899 4.0 252.820795 5.0 217.116682 6.0 226.887020 7.0 8372.760020 8.0 679.262724 9.0 6664.305398 10.0 303.836865 11.0 38.992775 12.0 370.555945 13.0 225.426578 14.0 792.552177 dtype: float64, 0.0 0.023120 1.0 957.398018 2.0 1653.130656 3.0 312.674751 4.0 232.042786 5.0 46.358226 6.0 88.036711 7.0 8755.643506 8.0 559.566440 9.0 8355.370538 10.0 193.387755 11.0 41.666138 12.0 337.049793 13.0 483.547430 14.0 773.972782 dtype: float64, 0.0 0.033293 1.0 969.626708 2.0 1297.529114 3.0 977.911912 4.0 259.779096 5.0 67.183360 6.0 89.737553 7.0 8854.214220 8.0 675.703910 9.0 8294.052712 10.0 238.218996 11.0 11.775540 12.0 324.088091 13.0 310.801546 14.0 680.063699 dtype: float64, 0.0 0.058879 1.0 766.412267 2.0 1118.088281 3.0 568.988670 4.0 353.606805 5.0 102.089673 6.0 94.437363 7.0 7941.017089 8.0 710.640359 9.0 7490.756050 10.0 230.317957 11.0 4.434703 12.0 244.390317 13.0 616.475587 14.0 869.942226 dtype: float64, 0.0 0.101420 1.0 1081.095656 2.0 1553.819009 3.0 495.689697 4.0 255.561483 5.0 154.728923 6.0 59.190176 7.0 8751.938363 8.0 585.682205 9.0 8367.908731 10.0 212.961123 11.0 8.980774 12.0 310.658917 13.0 489.046639 14.0 624.180927 dtype: float64, 0.0 0.028823 1.0 691.240406 2.0 1172.083468 3.0 676.462831 4.0 406.588582 5.0 234.986700 6.0 120.913183 7.0 7970.049707 8.0 813.971942 9.0 8282.878189 10.0 209.747680 11.0 4.449753 12.0 197.324463 13.0 341.816923 14.0 508.186220 dtype: float64, 0.0 0.024816 1.0 813.706783 2.0 1088.383745 3.0 614.937527 4.0 353.201238 5.0 81.006107 6.0 71.038596 7.0 7353.475552 8.0 397.453108 9.0 9028.332491 10.0 186.277880 11.0 6.773958 12.0 317.106377 13.0 201.442633 14.0 740.103340 dtype: float64, 0.0 0.021887 1.0 1037.210079 2.0 1411.281867 3.0 335.109484 4.0 281.539185 5.0 79.864511 6.0 83.170820 7.0 7817.391705 8.0 546.414268 9.0 6298.339781 10.0 230.595819 11.0 28.556676 12.0 276.767253 13.0 390.489644 14.0 561.430285 dtype: float64, 0.0 0.028207 1.0 651.342645 2.0 1180.759136 3.0 1441.020598 4.0 267.334673 5.0 138.863439 6.0 117.194400 7.0 8129.748315 8.0 414.446128 9.0 7868.265518 10.0 234.267416 11.0 2.951786 12.0 187.046167 13.0 1211.462668 14.0 630.434303 dtype: float64, 0.0 0.088627 1.0 777.016427 2.0 1293.328843 3.0 761.269775 4.0 282.519880 5.0 218.647049 6.0 176.553431 7.0 8195.876899 8.0 452.575079 9.0 7810.309519 10.0 190.375815 11.0 6.328983 12.0 284.079838 13.0 891.924271 14.0 547.441518 dtype: float64, 0.0 0.020654 1.0 661.425510 2.0 983.462567 3.0 628.072889 4.0 193.003218 5.0 148.020924 6.0 141.622947 7.0 7105.716476 8.0 171.562336 9.0 6680.934603 10.0 178.822268 11.0 5.474149 12.0 183.200154 13.0 237.551761 14.0 617.529937 dtype: float64, 0.0 0.021425 1.0 711.453916 2.0 920.990414 3.0 297.181334 4.0 296.385959 5.0 133.816147 6.0 171.932500 7.0 7115.781325 8.0 588.948971 9.0 6600.411750 10.0 186.477262 11.0 2.043274 12.0 264.922625 13.0 371.917709 14.0 756.622374 dtype: float64, 0.0 0.029594 1.0 857.402464 2.0 1294.707535 3.0 220.909884 4.0 214.864352 5.0 174.392236 6.0 131.431768 7.0 7035.254183 8.0 555.915840 9.0 8906.501784 10.0 176.915413 11.0 11.997275 12.0 336.661245 13.0 398.958064 14.0 934.283322 dtype: float64, 0.0 0.020192 1.0 760.193479 2.0 1099.969645 3.0 549.725686 4.0 157.682263 5.0 91.053498 6.0 98.471660 7.0 7295.150872 8.0 350.450071 9.0 7234.452273 10.0 122.316578 11.0 18.823908 12.0 262.661157 13.0 172.461782 14.0 660.146877 dtype: float64, 0.0 0.112364 1.0 635.649273 2.0 1128.680668 3.0 781.645362 4.0 197.314956 5.0 229.777608 6.0 96.903604 7.0 5933.490065 8.0 596.786704 9.0 5535.905886 10.0 169.179818 11.0 7.729125 12.0 427.107822 13.0 641.705644 14.0 460.291865 dtype: float64]
type(annual_biome[0])
pandas.core.series.Series
denominador = np.log1p(max(map(max,annual_biome)))
for yr in range(17):
fig, ax = plt.subplots(figsize=(12, 8), frameon= False)
for spine in plt.gca().spines.values():
spine.set_visible(False)
cmap = plt.get_cmap('OrRd')
min_val = 0
max_val = 1
norm = cls.Normalize(min_val, max_val)
cmmapable = cm.ScalarMappable(norm, cmap)
cmmapable.set_array(range(min_val, max_val))
ecos_info, ecos = get_ecos()
total_trans = np.log1p(annual_biome[yr])/denominador
patches = []
for info, ec in zip(ecos_info,ecos):
patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans.loc[info['BIOME_NUM']] )))) if info['BIOME_NAME'] \
!= 'N/A' else patches.append(Polygon(np.array(ec),True, color = cmap(norm(total_trans.iloc[0] )))) #cuidado estou a eliminar rock
ax.add_collection(PatchCollection(patches, match_original= True, zorder=2))
title = 'Biomes Index Burned' + years[yr]
cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal")
cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig('biomeslogtransform' + years[yr], dpi=1200, bbox_inches='tight')
annual_biome[0]
0.0 0.070748 1.0 476.112748 2.0 946.678098 3.0 382.337005 4.0 193.003218 5.0 49.000975 6.0 60.003140 7.0 8077.196531 8.0 708.472528 9.0 6661.132970 10.0 210.577024 11.0 7.037331 12.0 264.166778 13.0 881.544288 14.0 280.317529 dtype: float64
max(map(max,annual_biome))
10757.476236875702
open('df_finall','wb').write(pickle.dumps(df_finall))
463793
lala = pickle.loads(open('df_finall','rb').read())
pwd
'C:\\Users\\alpha\\Documents\\TestEco2017'
int(ecos_info[0]['BIOME_NUM'])
11